Yi Xu

AAAI Conference 2026 Conference Paper

Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds

• Xuesong Jia
• Yuanjie Shi
• Ziquan Liu
• Yi Xu
• Yan Yan

Conformal prediction (CP) is a general framework to quantify the predictive uncertainty of machine learning models that uses a set prediction to include the true label with a valid probability. To align the uncertainty measured by CP, conformal training methods minimize the size of the prediction sets. A typical way is to use a surrogate indicator function, usually Sigmoid or Gaussian error function. However, these surrogate functions do not have a uniform error bound to the indicator function, leading to uncontrollable learning bounds. In this paper, we propose a simple cost-sensitive conformal training algorithm that does not rely on the indicator approximation mechanism. Specifically, we theoretically show that minimizing the expected size of prediction sets is upper bounded by the expected rank of true labels. To this end, we develop an importance weighting strategy that assigns the weight using the rank of true label on each data. Our analysis provably demonstrates the tightness between the proposed weighted objective and the expected size of conformal prediction sets. Extensive experiments verify the validity of our theoretical insights, and superior empirical performance over other conformal training in terms of predictive efficiency with 21.38% reduction for average prediction set size.

AAAI Conference 2026 Conference Paper

Distillation-Guided Structural Transfer for Continual Learning Beyond Sparse Distributed Memory

• Huiyan Xue
• Xuming Ran
• Yaxin Li
• Qi Xu
• Enhui Li
• Yi Xu
• Qiang Zhang

Sparse neural systems are gaining traction for efficient continual learning due to their modularity and low interference. Architectures like Sparse Distributed Memory Multi-Layer Perceptrons (SDMLP) construct task-specific subnetworks via Top-K activation and have shown resilience against catastrophic forgetting. However, their rigid modularity poses two fundamental challenges: (1) the isolation of sparse subnetworks severely limits cross-task knowledge reuse; and (2) increased sparsity reduces interference but often degrades performance due to constrained feature sharing.We propose Selective Subnetwork Distillation (SSD), a structurally guided continual learning framework that treats distillation not as a regularizer, but as a topology-aligned information conduit. By identifying neurons with high activation frequency, SSD selectively distills knowledge within previous Top-K subnetworks and output logits—without requiring replay or task labels—preserving both sparsity and functional specialization.Unlike conventional distillation, SSD operates under hard modular constraints and enables structural realignment without altering the sparse architecture.While our method is validated on SDMLP, its structure-aligned mechanism has the potential to generalize to other sparse networks as a plug-in module for promoting representation sharing.Comprehensive experiments on Split CIFAR-10, CIFAR-100, and MNIST demonstrate that SSD improves accuracy, retention, and manifold coverage, offering a structurally grounded solution to sparse continual learning.

AAAI Conference 2026 Conference Paper

Score-Based Model for Low-Rank Tensor Recovery

• Zhengyun Cheng
• Changhao Wang
• Guanwen Zhang
• Yi Xu
• Wei Zhou
• Xiangyang Ji

Low-rank tensor decompositions (TDs) provide an effective framework for multiway data analysis. Traditional TD methods rely on predefined structural assumptions, such as CP or Tucker decompositions. From a probabilistic perspective, these methods effectively model the relationships between latent factors and the low-rank tensor using Dirac delta distributions. However, tensor low-rank decomposition is inherently non-unique, leading to a multimodal distribution over possible solutions. Critically, such prior knowledge is rarely available in practical scenarios, particularly regarding the optimal rank structure and contraction rules. To address this issue, we propose a score-based model that eliminates the need for predefined structural or distributional assumptions, enabling the learning of compatibility between tensors and latent factors. Specifically, a neural network is designed to learn the energy function, which is optimized via score matching to capture the gradient of the joint log-probability of tensor entries and latent factors. Our method allows for modeling structures and distributions beyond the Dirac delta assumption. Moreover, integrating the block coordinate descent (BCD) algorithm with the proposed smooth regularization enables the model to perform both tensor completion and denoising. Experimental results demonstrate significant performance improvements across various tensor types, including sparse and continuous-time tensors, as well as visual data.

IROS Conference 2025 Conference Paper

A Skill-Based Hierarchical Framework with Dangerous Action Masking for Autonomous Navigation of Jumping Robots

• Gangyang Li
• Qijie Zhou
• Yi Xu
• Weitao Zhang
• Qing Shi

Achieving autonomous navigation for biologically inspired jumping robots remains a long-standing challenge, due to the inherent instability of jumping motions and the limitations in onboard sensor capabilities. This paper proposes a skill-based hierarchical framework with dangerous action masking (SH-DAM) for autonomous navigation of jumping robot. The framework, based on hierarchical reinforcement learning, includes a low-level controller that learns locomotion skills (crawling, turning and jumping) to overcome various obstacles. A high-level controller selects and coordinates these skills, while also incorporating curriculum learning to enhance the performance of navigation tasks. For safe navigation, we utilize dangerous action masking to suppress the probability of selecting jump motions in dangerous regions. We improved the locust-inspired jumping robot platform JumpBot-S, by integrating a lightweight time-of-flight (ToF) sensor, and constructed a range of complex environments for experiments. Simulation results demonstrate that SH-DAM enables the robot to autonomously complete challenging navigation tasks. Compared to baseline algorithms, our method achieves a 12. 57% increase in success rate, a 55. 88% reduction in stuck rate, and a 57. 89% reduction in rollover rate. Finally, we deployed our framework in real-world environments and conducted experiments in both normal lit and dimly lit conditions. This framework provides a new paradigm for jumping robot navigation in complex environments.

AAAI Conference 2025 Conference Paper

BIG-FUSION: Brain-Inspired Global-Local Context Fusion Framework for Multimodal Emotion Recognition in Conversations

• Yusong Wang
• Xuanye Fang
• Huifeng Yin
• Dongyuan Li
• Guoqi Li
• Qi Xu
• Yi Xu
• Shuai Zhong

Considering the importance of capturing both global conversational topics and local speaker dependencies for multimodal emotion recognition in conversations, current approaches first utilize sequence models like Transformer to extract global context information, then apply Graph Neural Networks to model local speaker dependencies for local context information extraction, coupled with Graph Contrastive Learning (GCL) to enhance node representation learning. However, this sequential design introduces potential biases: the extracted global context information inevitably influences subsequent processing, compromising the independence and diversity of the original local features; current graph augmentation methods in GCL cannot consider both global and local context information in conversations to evaluate the node importance, hindering the learning of key information. Inspired by the human brain excels at handling complex tasks by efficiently integrating local and global information processing mechanisms, we propose an aligned global-local context fusion framework for sequence-based design to address these problems. This design includes a dual-attention Transformer and a dual-evaluation method for graph augmentation in GCL. The dual-attention Transformer combines global attention for overall context extraction with sliding-window attention for local context capture, both enhanced by spiking neuron dynamics. The dual-evaluation method in GCL comprises global importance evaluation to identify nodes crucial for overall conversation context, and local importance evaluation to detect nodes significant for local semantics, generating augmented graph views that preserve both global and local information. This approach ensures balanced information processing throughout the pipeline, enhancing biological plausibility and achieving superior emotion recognition.

NeurIPS Conference 2025 Conference Paper

ChatVLA-2: Vision-Language-Action Model with Open-World Reasoning

• Zhongyi Zhou
• Yichen Zhu
• Xiaoyu Liu
• Zhibin Tang
• Junjie Wen
• Yaxin Peng
• Chaomin Shen
• Yi Xu

Vision-language-action (VLA) models have emerged as the next generation of models in robotics. However, despite leveraging powerful pre-trained Vision-Language Models (VLMs), existing end-to-end VLA systems often lose key capabilities during fine-tuning as the model adapts to specific robotic tasks. We argue that a generalizable VLA model should retain and expand upon the VLM's core competencies: 1) **Open-world reasoning** - the VLA should inherit the knowledge from VLM, i. e. , recognize anything that the VLM can recognize, capable of solving math problems, possessing visual-spatial intelligence, 2) **Reasoning following** – effectively translating the open-world reasoning into actionable steps for the robot. In this work, we introduce **ChatVLA-2**, a novel mixture-of-expert VLA model coupled with a specialized three-stage training pipeline designed to preserve the VLM’s original strengths while enabling actionable reasoning. To validate our approach, we design a math-matching task wherein a robot interprets math problems written on a whiteboard and picks corresponding number cards from a table to solve equations. Remarkably, our method exhibits exceptional mathematical reasoning and OCR capabilities, despite these abilities not being explicitly trained within the VLA. Furthermore, we demonstrate that the VLA possesses strong spatial reasoning skills, enabling it to interpret novel directional instructions involving previously unseen objects. Overall, our method showcases reasoning and comprehension abilities that significantly surpass state-of-the-art imitation learning methods such as OpenVLA, DexVLA, and $\pi_0$. This work represents a substantial advancement toward developing truly generalizable robotic foundation models endowed with robust reasoning capacities.

EAAI Journal 2025 Journal Article

Hybrid semantic-structural graph neural network for fault knowledge graph completion in railway operational equipment

• Xiaorui Yang
• Honghui Li
• Yi Xu
• Yunhao Deng
• Yanhui Bai
• Shufang Liu

Incomplete fault knowledge graphs in railway operational equipment hinder effective fault diagnosis, prediction, and maintenance planning. This study addresses the challenge of completing fault knowledge graph by proposing a hybrid semantic-structural graph neural network (Hybrid S-GNN) that integrates both semantic and structural information for knowledge graph completion (KGC) in railway fault scenarios. The Hybrid S-GNN comprises four key modules: a semantic encoding module that enhances textual fault data representations through contextual enhancement and dynamic weight allocation; a structural encoding module that captures graph topology using multi-view structure encoding combining local aggregation, global path encoding, and relation-aware adjustment; a semantic-structural fusion module leveraging attention mechanisms to balance semantic and structural signals; and an optimization-prediction module employing margin-based ranking loss and context-aware negative sampling for accurate triple prediction. Experiments on real-world railway fault knowledge graphs demonstrate that Hybrid S-GNN achieves a Hits@10 of 80. 5 % and mean reciprocal rank (MRR) of 0. 640, outperforming state-of-the-art baselines by 5. 8 % and 6. 1 %, respectively. Ablation studies confirm the critical contributions of each module, validating the necessity of jointly modeling semantic and structural features. This work provides an effective solution to enhance railway fault knowledge graphs and paves the way for advanced fault management applications in railway operations.

ICML Conference 2025 Conference Paper

Investigating Non-Transitivity in LLM-as-a-Judge

• Yi Xu
• Laura Ruis
• Tim Rocktäschel
• Robert Kirk

Automatic evaluation methods based on large language models (LLMs) are emerging as the standard tool for assessing the instruction-following abilities of LLM-based agents. The most common method in this paradigm, pairwise comparisons with a baseline model, critically depends on the assumption of transitive preferences. However, the validity of this assumption remains largely unexplored. In this study, we investigate the presence of non-transitivity within the AlpacaEval framework and analyze its effects on model rankings. We find that LLM judges exhibit non-transitive preferences, leading to rankings that are sensitive to the choice of the baseline model. To mitigate this issue, we show that round-robin tournaments combined with Bradley-Terry models of preference can produce more reliable rankings. Notably, our method increases both the Spearman correlation and the Kendall correlation with Chatbot Arena (95. 0% $\rightarrow$ 96. 4% and 82. 1% $\rightarrow$ 86. 3% respectively). To address the computational cost of round-robin tournaments, we propose Swiss-Wise Iterative Matchmaking (Swim) tournaments, using a dynamic matching strategy to capture the benefits of round-robin tournaments while maintaining computational efficiency.

NeurIPS Conference 2025 Conference Paper

MINGLE: Mixture of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging

• Zihuan Qiu
• Yi Xu
• Chiyuan He
• Fanman Meng
• Linfeng Xu
• Qingbo Wu
• Hongliang Li

Continual model merging integrates independently fine-tuned models sequentially without access to the original training data, offering a scalable and efficient solution for continual learning. However, existing methods face two critical challenges: parameter interference among tasks, which leads to catastrophic forgetting, and limited adaptability to evolving test distributions. To address these issues, we introduce the task of Test-Time Continual Model Merging (TTCMM), which leverages a small set of unlabeled test samples during inference to alleviate parameter conflicts and handle distribution shifts. We propose MINGLE, a novel framework for TTCMM. MINGLE employs a mixture-of-experts architecture with parameter-efficient, low-rank experts, which enhances adaptability to evolving test distributions while dynamically merging models to mitigate conflicts. To further reduce forgetting, we propose Null-Space Constrained Gating, which restricts gating updates to subspaces orthogonal to prior task representations, thereby suppressing activations on old tasks and preserving past knowledge. We further introduce an Adaptive Relaxation Strategy that adjusts constraint strength dynamically based on interference signals observed during test-time adaptation, striking a balance between stability and adaptability. Extensive experiments on standard continual merging benchmarks demonstrate that MINGLE achieves robust generalization, significantly reduces forgetting, and consistently surpasses previous state-of-the-art methods by 7–9% on average across diverse task orders. Our code is available at: https: //github. com/zihuanqiu/MINGLE

ECAI Conference 2025 Conference Paper

PedDet: Adaptive Spectral Optimization for Multimodal Pedestrian Detection

• Rui Zhao
• Zeyu Zhang 0006
• Yi Xu
• Yi Yao
• Yan Huang
• Wenxin Zhang 0005
• Zirui Song
• Xiuying Chen

Pedestrian detection in intelligent transportation systems has made significant progress but faces two critical challenges: (1) insufficient fusion of complementary information between visible and infrared spectra, particularly in complex scenarios, and (2) sensitivity to illumination changes, such as low-light or overexposed conditions, leading to degraded performance. To address these issues, we propose PedDet, an adaptive spectral optimization complementarity framework which specifically enhanced and optimized for multispectral pedestrian detection. PedDet introduces the Multi-scale Spectral Feature Perception Module (MSFPM) to adaptively fuse visible and infrared features, enhancing robustness and flexibility in feature extraction. Additionally, the Illumination Robustness Feature Decoupling Module (IRFDM) improves detection stability under varying lighting by decoupling pedestrian and background features. We further design a contrastive alignment to enhance intermodal feature discrimination. Experiments on LLVIP and MSDS datasets demonstrate that PedDet achieves state-of-the-art performance, improving the mAP by 6. 6 % with superior detection accuracy even in low-light conditions, marking a significant step forward for road safety.

IJCAI Conference 2025 Conference Paper

Predicting Spectral Information for Self-Supervised Signal Classification

• Yi Xu
• Shuang Wang
• Hantong Xing
• Chenxu Wang
• Dou Quan
• Rui Yang
• Dong Zhao
• Luyang Mei

Deep learning methods have demonstrated remarkable performance across various communication signal processing tasks. However, most signal classification methods require a substantial amount of labeled samples for training, posing significant challenges in the field of communication signals, as labeling necessitates expert knowledge. This paper proposes a novel self-supervised signal classification method called Spectral-Guided Self-Supervised Signal Classification (SGSSC). Specifically, to leverage frequency-domain information with modulation semantics as prior knowledge for the model, we design a previously unexplored pretext task tailored to the format of signal data. This task involves predicting spectral information from masked time-domain signals, enabling the model to learn implicit signal features through cross-domain pattern transformation. Furthermore, the pretext task in the SGSSC method is relevant to the downstream classification task, and using traditional fine-tuning strategies on the downstream task may lead to the loss of certain features associated with the pretext task. Therefore, we propose an attention mechanism-based fine-tuning strategy that adaptively integrates pre-trained features from different levels. Extensive experimental results validate the superiority of the SGSSC method. For instance, when the proportion of labeled samples is only 0. 5%, our method achieves an average improvement of 2. 3% in downstream classification tasks compared to the best-performing self-supervised training strategies.

AAAI Conference 2025 Conference Paper

SpikingYOLOX: Improved YOLOX Object Detection with Fast Fourier Convolution and Spiking Neural Networks

• Wei Miao
• Jiangrong Shen
• Qi Xu
• Timo Hamalainen
• Yi Xu
• Fengyu Cong

In recent years, with the advancements in brain science, spiking neural networks (SNNs) have garnered significant attention. SNNs can generate spikes that mimic the function of neurons transmission in humans brain, thereby significantly reducing computational costs by the event-driven nature during training. While deep SNNs have shown impressive performance on classification tasks, they still face challenges in more complex tasks such as object detection. In this paper, we propose SpikingYOLOX, extending the structure of the original YOLOX by introducing signed spiking neurons and fast Fourier convolution (FFC). The designed ternary signed spiking neurons could generate three kinds of spikes to obtain more robust features in the deep layer of the backbone. Meanwhile, we integrate FFC with SNN modules to enhance object detection performance, because its global receptive field is beneficial to the object detection task. Extensive experiments demonstrate that the proposed SpikingYOLOX achieves state-of-the-art performance among other SNN-based object detection methods.

ICRA Conference 2025 Conference Paper

Uncertainty-Guided Enhancement on Driving Perception System Via Foundation Models

• Yunhao Yang
• Yuxin Hu
• Mao Ye
• Zaiwei Zhang
• Zhichao Lu
• Yi Xu
• Ufuk Topcu
• Ben Snyder

Multimodal foundation models offer promising advancements for enhancing driving perception systems, but their high computational and financial costs pose challenges. We develop a method that leverages foundation models to refine predictions from existing driving perception modelssuch as enhancing object classification accuracy-while minimizing the frequency of using these resource-intensive models. The method quantitatively characterizes uncertainties in the perception model's predictions and engages the foundation model only when these uncertainties exceed a pre-specified threshold. Specifically, it characterizes uncertainty by calibrating the perception model's confidence scores into theoretical lower bounds on the probability of correct predictions using conformal prediction. Then, it sends images to the foundation model and queries for refining the predictions only if the theoretical bound of the perception model's outcome is below the threshold. Additionally, we propose a temporal inference mechanism that enhances prediction accuracy by integrating historical predictions, leading to tighter theoretical bounds. The method demonstrates a 10 to 15 percent improvement in prediction accuracy and reduces the number of queries to the foundation model by 50 percent, based on quantitative evaluations from driving datasets.

NeurIPS Conference 2025 Conference Paper

Understanding while Exploring: Semantics-driven Active Mapping

• Liyan Chen
• Huangying Zhan
• Hairong Yin
• Yi Xu
• Philippos Mordohai

Effective robotic autonomy in unknown environments demands proactive exploration and precise understanding of both geometry and semantics. In this paper, we propose ActiveSGM, an active semantic mapping framework designed to predict the informativeness of potential observations before execution. Built upon a 3D Gaussian Splatting (3DGS) mapping backbone, our approach employs semantic and geometric uncertainty quantification, coupled with a sparse semantic representation, to guide exploration. By enabling robots to strategically select the most beneficial viewpoints, ActiveSGM efficiently enhances mapping completeness, accuracy, and robustness to noisy semantic data, ultimately supporting more adaptive scene exploration. Our experiments on the Replica and Matterport3D datasets highlight the effectiveness of ActiveSGM in active semantic mapping tasks.

AAAI Conference 2024 Conference Paper

Diverse and Stable 2D Diffusion Guided Text to 3D Generation with Noise Recalibration

• Xiaofeng Yang
• Fayao Liu
• Yi Xu
• Hanjing Su
• Qingyao Wu
• Guosheng Lin

In recent years, following the success of text guided image generation, text guided 3D generation has gained increasing attention among researchers. Dreamfusion is a notable approach that enhances generation quality by utilizing 2D text guided diffusion models and introducing SDS loss, a technique for distilling 2D diffusion model information to train 3D models. However, the SDS loss has two major limitations that hinder its effectiveness. Firstly, when given a text prompt, the SDS loss struggles to produce diverse content. Secondly, during training, SDS loss may cause the generated content to overfit and collapse, limiting the model's ability to learn intricate texture details. To overcome these challenges, we propose a novel approach called Noise Recalibration algorithm. By incorporating this technique, we can generate 3D content with significantly greater diversity and stunning details. Our approach offers a promising solution to the limitations of SDS loss.

NeurIPS Conference 2024 Conference Paper

Facilitating Multimodal Classification via Dynamically Learning Modality Gap

• Yang Yang
• Fengqiang Wan
• Qing-Yuan Jiang
• Yi Xu

Multimodal learning falls into the trap of the optimization dilemma due to the modality imbalance phenomenon, leading to unsatisfactory performance in real applications. A core reason for modality imbalance is that the models of each modality converge at different rates. Many attempts naturally focus on adjusting learning procedures adaptively. Essentially, the reason why models converge at different rates is because the difficulty of fitting category labels is inconsistent for each modality during learning. From the perspective of fitting labels, we find that appropriate positive intervention label fitting can correct this difference in learning ability. By exploiting the ability of contrastive learning to intervene in the learning of category label fitting, we propose a novel multimodal learning approach that dynamically integrates unsupervised contrastive learning and supervised multimodal learning to address the modality imbalance problem. We find that a simple yet heuristic integration strategy can significantly alleviate the modality imbalance phenomenon. Moreover, we design a learning-based integration strategy to integrate two losses dynamically, further improving the performance. Experiments on widely used datasets demonstrate the superiority of our method compared with state-of-the-art (SOTA) multimodal learning approaches. The code is available at https: //github. com/njustkmg/NeurIPS24-LFM.

IJCAI Conference 2024 Conference Paper

FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning

• Yi Xu
• Ying Li
• Haoyu Luo
• Xiaoliang Fan
• Xiao Liu

In real-world situations, federated learning often needs to process non-IID (non-independent and identically distributed) data with multiple skews, causing inadequate model performance. Existing federated learning methods mainly focus on addressing the problem with a single skew of non-IID, and hence the performance of global models can be degraded when faced with dual skewed non-IID data caused by heterogeneous label distributions and sample sizes among clients. To address the problem with dual skewed non-IID data, in this paper, we propose a federated learning algorithm based on local graph, named FBLG. Specifically, to address the label distribution skew, we firstly construct a local graph based on clients' local losses and Jensen-Shannon (JS) divergence, so that similar clients can be selected for aggregation to ensure a highly consistent global model. Afterwards, to address the sample size skew, we design the objective function to favor clients with more samples as models trained with more samples tend to carry more useful information. Experiments on four datasets with dual skewed non-IID data demonstrate FBLG outperforms nine baseline methods and achieves up to 9% improvement in accuracy. Simultaneously, both theoretical analysis and experiments show FBLG can converge quickly.

NeurIPS Conference 2024 Conference Paper

HuRef: HUman-REadable Fingerprint for Large Language Models

• Boyi Zeng
• Lizheng Wang
• Yuncong Hu
• Yi Xu
• Chenghu Zhou
• Xinbing Wang
• Yu Yu
• Zhouhan Lin

Protecting the copyright of large language models (LLMs) has become crucial due to their resource-intensive training and accompanying carefully designed licenses. However, identifying the original base model of an LLM is challenging due to potential parameter alterations. In thisstudy, we introduce HuRef, a human-readable fingerprint for LLMs that uniquely identifies the base model without interfering with training or exposing model parameters to the public. We first observe that the vector direction of LLM parameters remains stable after the model has converged during pretraining, with negligible perturbations through subsequent training steps, including continued pretraining, supervised fine-tuning, and RLHF, which makes it a sufficient conditionto identify the base model. The necessity is validated by continuing to train an LLM with an extra term to drive away the model parameters' direction and the model becomes damaged. However, this direction is vulnerable to simple attacks like dimension permutation or matrix rotation, which significantly change it without affecting performance. To address this, leveraging the Transformer structure, we systematically analyze potential attacks and define three invariant terms that identify an LLM's base model. Due to the potential risk of information leakage, we cannot publish invariant terms directly. Instead, we map them to a Gaussian vector using an encoder, then convert it into a natural image using StyleGAN2, and finally publish the image. In our black-box setting, all fingerprinting steps are internally conducted by the LLMs owners. To ensure the published fingerprints are honestly generated, we introduced Zero-Knowledge Proof (ZKP). Experimental results across various LLMs demonstrate the effectiveness of our method. The code is available at https: //github. com/LUMIA-Group/HuRef.

AAAI Conference 2024 Conference Paper

Multi-Region Text-Driven Manipulation of Diffusion Imagery

• Yiming Li
• Peng Zhou
• Jun Sun
• Yi Xu

Text-guided image manipulation has attracted significant attention recently. Prevailing techniques concentrate on image attribute editing for individual objects, however, encountering challenges when it comes to multi-object editing. The main reason is the lack of consistency constraints on the spatial layout. This work presents a multi-region guided image manipulation framework, enabling manipulation through region-level textual prompts. With MultiDiffusion as a baseline, we are dedicated to the automatic generation of a rational multi-object spatial distribution, where disparate regions are fused as a unified entity. To mitigate interference from regional fusion, we employ an off-the-shelf model (CLIP) to impose region-aware spatial guidance on multi-object manipulation. Moreover, when applied to the StableDiffusion, the presence of quality-related yet object-agnostic lengthy words hampers the manipulation. To ensure focus on meaningful object-specific words for efficient guidance and generation, we introduce a keyword selection method. Furthermore, we demonstrate a downstream application of our method for multi-region inversion, which is tailored for manipulating multiple objects in real images. Our approach, compatible with variants of Stable Diffusion models, is readily applicable for manipulating diverse objects in extensive images with high-quality generation, showing superb image control capabilities. Code is available at https://github.com/liyiming09/multi-region-guided-diffusion.

NeurIPS Conference 2024 Conference Paper

Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models

• Yilun Jin
• Zheng Li
• Chenwei Zhang
• Tianyu Cao
• Yifan Gao
• Pratik Jayarao
• Mao Li
• Xin Liu

Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping by alleviating task-specific engineering efforts and by providing users with interactive conversations. Despite the potential, LLMs face unique challenges in online shopping, such as domain-specific concepts, implicit knowledge, and heterogeneous user behaviors. Motivated by the potential and challenges, we propose Shopping MMLU, a diverse multi-task online shopping benchmark derived from real-world Amazon data. Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants. With Shoppping MMLU, we benchmark over 20 existing LLMs and uncover valuable insights about practices and prospects of building versatile LLM-based shop assistants. Shopping MMLU can be publicly accessed at https: //github. com/KL4805/ShoppingMMLU. In addition, with Shopping MMLU, we are hosting a competition in KDD Cup 2024 with over 500 participating teams. The winning solutions and the associated workshop can be accessed at our website https: //amazon-kddcup24. github. io/.

IROS Conference 2024 Conference Paper

Skeleton-Based Human Action Recognition with Noisy Labels

• Yi Xu
• Kunyu Peng
• Di Wen 0006
• Ruiping Liu 0001
• Junwei Zheng
• Yufan Chen 0001
• Jiaming Zhang 0001
• Alina Roitberg

Understanding human actions from body poses is critical for assistive robots sharing space with humans in order to make informed and safe decisions about the next interaction. However, precise temporal localization and annotation of activity sequences is time-consuming and the resulting labels are often noisy. If not effectively addressed, label noise negatively affects the model’s training, resulting in lower recognition quality. Despite its importance, addressing label noise for skeleton-based action recognition has been overlooked so far. In this study, we bridge this gap by implementing a framework that augments well-established skeleton-based human action recognition methods with label-denoising strategies from various research areas to serve as the initial benchmark. Observations reveal that these baselines yield only marginal performance when dealing with sparse skeleton data. Consequently, we introduce a novel methodology, NoiseEraSAR, which integrates global sample selection, co-teaching, and Cross-Modal Mixture-of-Experts (CM-MOE) strategies, aimed at mitigating the adverse impacts of label noise. Our proposed approach demonstrates better performance on the established benchmark, setting new state-of-the-art standards. The source code for this study will be made accessible at https://github.com/xuyizdby/NoiseEraSAR.

TMLR Journal 2023 Journal Article

Attentional-Biased Stochastic Gradient Descent

• Qi Qi
• Yi Xu
• Wotao Yin
• Rong Jin
• Tianbao Yang

In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple modification to momentum SGD where we assign an individual importance weight to each sample in the mini-batch. The individual-level weight of a sampled data is systematically proportional to the exponential of a scaled loss value of the data, where the scaling factor is interpreted as the regularization parameter in the framework of distributionally robust optimization (DRO). Depending on whether the scaling factor is positive or negative, ABSGD is guaranteed to converge to a stationary point of an information-regularized min-max or min-min DRO problem, respectively. Compared with existing class-level weighting schemes, our method can capture the diversity between individual examples within each class. Compared with existing individual-level weighting methods using meta-learning that require three backward propagations for computing mini-batch stochastic gradients, our method is more efficient with only one backward propagation at each iteration as in standard deep learning methods. ABSGD is flexible enough to combine with other robust losses without any additional cost. Our empirical studies on several benchmark datasets demonstrate the effectiveness of the proposed method.

NeurIPS Conference 2023 Conference Paper

Latent Graph Inference with Limited Supervision

• Jianglin Lu
• Yi Xu
• Huan Wang
• Yue Bai
• Yun Fu

Latent graph inference (LGI) aims to jointly learn the underlying graph structure and node representations from data features. However, existing LGI methods commonly suffer from the issue of supervision starvation, where massive edge weights are learned without semantic supervision and do not contribute to the training loss. Consequently, these supervision-starved weights, which determine the predictions of testing samples, cannot be semantically optimal, resulting in poor generalization. In this paper, we observe that this issue is actually caused by the graph sparsification operation, which severely destroys the important connections established between pivotal nodes and labeled ones. To address this, we propose to restore the corrupted affinities and replenish the missed supervision for better LGI. The key challenge then lies in identifying the critical nodes and recovering the corrupted affinities. We begin by defining the pivotal nodes as k-hop starved nodes, which can be identified based on a given adjacency matrix. Considering the high computational burden, we further present a more efficient alternative inspired by CUR matrix decomposition. Subsequently, we eliminate the starved nodes by reconstructing the destroyed connections. Extensive experiments on representative benchmarks demonstrate that reducing the starved nodes consistently improves the performance of state-of-the-art LGI methods, especially under extremely limited supervision (6. 12% improvement on Pubmed with a labeling rate of only 0. 3%).

AAAI Conference 2023 Conference Paper

Mining and Applying Composition Knowledge of Dance Moves for Style-Concentrated Dance Generation

• Xinjian Zhang
• Su Yang
• Yi Xu
• Weishan Zhang
• Longwen Gao

Choreography refers to creation of dance motions according to both music and dance knowledge, where the created dances should be style-specific and consistent. However, most of the existing methods generate dances using the given music as the only reference, lacking the stylized dancing knowledge, namely, the flag motion patterns contained in different styles. Without the stylized prior knowledge, these approaches are not promising to generate controllable style or diverse moves for each dance style, nor new dances complying with stylized knowledge. To address this issue, we propose a novel music-to-dance generation framework guided by style embedding, considering both input music and stylized dancing knowledge. These style embeddings are learnt representations of style-consistent kinematic abstraction of reference dance videos, which can act as controllable factors to impose style constraints on dance generation in a latent manner. Hence, we can make the style embedding fit into any given style while allowing the flexibility to generate new compatible dance moves by modifying the style embedding according to the learnt representations of a certain style. We are the first to achieve knowledge-driven style control in dance generation tasks. To support this study, we build a large multi-style music-to-dance dataset referred to as I-Dance. The qualitative and quantitative evaluations demonstrate the advantage of the proposed framework, as well as the ability to synthesize diverse moves under a dance style directed by style embedding.

NeurIPS Conference 2023 Conference Paper

Not All Out-of-Distribution Data Are Harmful to Open-Set Active Learning

• Yang Yang
• Yuxuan Zhang
• Xin Song
• Yi Xu

Active learning (AL) methods have been proven to be an effective way to reduce the labeling effort by intelligently selecting valuable instances for annotation. Despite their great success with in-distribution (ID) scenarios, AL methods suffer from performance degradation in many real-world applications because out-of-distribution (OOD) instances are always inevitably contained in unlabeled data, which may lead to inefficient sampling. Therefore, several attempts have been explored open-set AL by strategically selecting pure ID instances while filtering OOD instances. However, concentrating solely on selecting pseudo-ID instances may cause the training constraint of the ID classifier and OOD detector. To address this issue, we propose a simple yet effective sampling scheme, Progressive Active Learning (PAL), which employs a progressive sampling mechanism to leverage the active selection of valuable OOD instances. The proposed PAL measures unlabeled instances by synergistically evaluating instances' informativeness and representativeness, and thus it can balance the pseudo-ID and pseudo-OOD instances in each round to enhance both the capacity of the ID classifier and the OOD detector. %Meanwhile, PAL measures unlabeled instances by synergistically evaluating instances' informativeness and representativeness, which can more effectively estimate the values of instances. Extensive experiments on various open-set AL scenarios demonstrate the effectiveness of the proposed PAL, compared with the state-of-the-art methods. The code is available at \url{https: //github. com/njustkmg/PAL}.

NeurIPS Conference 2023 Conference Paper

OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects

• Isabella Liu
• Linghao Chen
• Ziyang Fu
• Liwen Wu
• Haian Jin
• Zhong Li
• Chin Ming Ryan Wong
• Yi Xu

We introduce OpenIllumination, a real-world dataset containing over 108K images of 64 objects with diverse materials, captured under 72 camera views and a large number of different illuminations. For each image in the dataset, we provide accurate camera parameters, illumination ground truth, and foreground segmentation masks. Our dataset enables the quantitative evaluation of most inverse rendering and material decomposition methods for real objects. We examine several state-of-the-art inverse rendering methods on our dataset and compare their performances. The dataset and code can be found on the project page: https: //oppo-us-research. github. io/OpenIllumination.

NeurIPS Conference 2023 Conference Paper

Supported Value Regularization for Offline Reinforcement Learning

• Yixiu Mao
• Hongchang Zhang
• Chen Chen
• Yi Xu
• Xiangyang Ji

Offline reinforcement learning suffers from the extrapolation error and value overestimation caused by out-of-distribution (OOD) actions. To mitigate this issue, value regularization approaches aim to penalize the learned value functions to assign lower values to OOD actions. However, existing value regularization methods lack a proper distinction between the regularization effects on in-distribution (ID) and OOD actions, and fail to guarantee optimal convergence results of the policy. To this end, we propose Supported Value Regularization (SVR), which penalizes the Q-values for all OOD actions while maintaining standard Bellman updates for ID ones. Specifically, we utilize the bias of importance sampling to compute the summation of Q-values over the entire OOD region, which serves as the penalty for policy evaluation. This design automatically separates the regularization for ID and OOD actions without manually distinguishing between them. In tabular MDP, we show that the policy evaluation operator of SVR is a contraction, whose fixed point outputs unbiased Q-values for ID actions and underestimated Q-values for OOD actions. Furthermore, the policy iteration with SVR guarantees strict policy improvement until convergence to the optimal support-constrained policy in the dataset. Empirically, we validate the theoretical properties of SVR in a tabular maze environment and demonstrate its state-of-the-art performance on a range of continuous control tasks in the D4RL benchmark.

AAAI Conference 2023 Conference Paper

Temporal Knowledge Graph Reasoning with Historical Contrastive Learning

• Yi Xu
• Junjie Ou
• Hui Xu
• Luoyi Fu

Temporal knowledge graph, serving as an effective way to store and model dynamic relations, shows promising prospects in event forecasting. However, most temporal knowledge graph reasoning methods are highly dependent on the recurrence or periodicity of events, which brings challenges to inferring future events related to entities that lack historical interaction. In fact, the current moment is often the combined effect of a small part of historical information and those unobserved underlying factors. To this end, we propose a new event forecasting model called Contrastive Event Network (CENET), based on a novel training framework of historical contrastive learning. CENET learns both the historical and non-historical dependency to distinguish the most potential entities that can best match the given query. Simultaneously, it trains representations of queries to investigate whether the current moment depends more on historical or non-historical events by launching contrastive learning. The representations further help train a binary classifier whose output is a boolean mask to indicate related entities in the search space. During the inference process, CENET employs a mask-based strategy to generate the final results. We evaluate our proposed model on five benchmark graphs. The results demonstrate that CENET significantly outperforms all existing methods in most metrics, achieving at least 8.3% relative improvement of Hits@1 over previous state-of-the-art baselines on event-based datasets.

NeurIPS Conference 2022 Conference Paper

Improved Fine-Tuning by Better Leveraging Pre-Training Data

• Ziquan Liu
• Yi Xu
• Yuanhong Xu
• Qi Qian
• Hao Li
• Xiangyang Ji
• Antoni Chan
• Rong Jin

As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studies have empirically shown that training from scratch has the final performance that is no worse than this pre-training strategy once the number of training samples is increased in some vision tasks. In this work, we revisit this phenomenon from the perspective of generalization analysis by using excess risk bound which is popular in learning theory. The result reveals that the excess risk bound may have a weak dependency on the pre-trained model. The observation inspires us to leverage pre-training data for fine-tuning, since this data is also available for fine-tuning. The generalization result of using pre-training data shows that the excess risk bound on a target task can be improved when the appropriate pre-training data is included in fine-tuning. With the theoretical motivation, we propose a novel selection strategy to select a subset from pre-training data to help improve the generalization on the target task. Extensive experimental results for image classification tasks on 8 benchmark data sets verify the effectiveness of the proposed data selection based fine-tuning pipeline. Our code is available at https: //github. com/ziquanliu/NeurIPS2022 UOT fine_tuning.

AAAI Conference 2022 Conference Paper

Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction

• Tiancheng Lin
• Hongteng Xu
• Canqian Yang
• Yi Xu

When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also its context in the corresponding bag. From the viewpoint of causal inference, such bag contextual prior works as a confounder and may result in model robustness and interpretability issues. Focusing on this problem, we propose a novel interventional multi-instance learning (IMIL) framework to achieve deconfounded instance-level prediction. Unlike traditional likelihood-based strategies, we design an Expectation- Maximization (EM) algorithm based on causal intervention, providing a robust instance selection in the training phase and suppressing the bias caused by the bag contextual prior. Experiments on pathological image analysis demonstrate that our IMIL method substantially reduces false positives and outperforms state-of-the-art MIL methods.

NeurIPS Conference 2022 Conference Paper

Look More but Care Less in Video Recognition

• Yitian Zhang
• Yue Bai
• Huan Wang
• Yi Xu
• Yun Fu

Existing action recognition methods typically sample a few frames to represent each video to avoid the enormous computation, which often limits the recognition performance. To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computation. Specifically, the Ample Branch takes all input frames to obtain abundant information with condensed computation and provides the guidance for Focal Branch by the proposed Navigation Module; the Focal Branch squeezes the temporal size to only focus on the salient frames at each convolution block; in the end, the results of two branches are adaptively fused to prevent the loss of information. With this design, we can introduce more frames to the network but cost less computation. Besides, we demonstrate AFNet can utilize less frames while achieving higher accuracy as the dynamic selection in intermediate features enforces implicit temporal modeling. Further, we show that our method can be extended to reduce spatial redundancy with even less cost. Extensive experiments on five datasets demonstrate the effectiveness and efficiency of our method.

IJCAI Conference 2022 Conference Paper

MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning

• Yi Xu
• Lichen Wang
• Yizhou Wang
• Can Qin
• Yulun Zhang
• Yun Fu

Few-shot learning aims to enable models generalize to new categories (query instances) with only limited labeled samples (support instances) from each category. Metric-based mechanism is a promising direction which compares feature embeddings via different metrics. However, it always fail to generalize to unseen domains due to the considerable domain gap challenge. In this paper, we propose a novel framework, MemREIN, which considers Memorized, Restitution, and Instance Normalization for cross-domain few-shot learning. Specifically, an instance normalization algorithm is explored to alleviate feature dissimilarity, which provides the initial model generalization ability. However, naively normalizing the feature would lose fine-grained discriminative knowledge between different classes. To this end, a memorized module is further proposed to separate the most refined knowledge and remember it. Then, a restitution module is utilized to restitute the discrimination ability from the learned knowledge. A novel reverse contrastive learning strategy is proposed to stabilize the distillation process. Extensive experiments on five popular benchmark datasets demonstrate that MemREIN well addresses the domain shift challenge, and significantly improves the performance up to 16. 43% compared with state-of-the-art baselines.

JBHI Journal 2022 Journal Article

MVSGAN: Spatial-Aware Multi-View CMR Fusion for Accurate 3D Left Ventricular Myocardium Segmentation

• Xiaoming Qi
• Yuting He
• Guanyu Yang
• Yang Chen
• Jian Yang
• Wangyag Liu
• Yinsu Zhu
• Yi Xu

The accurate 3D left ventricular (LV) myocardium segmentation in short-axis (SAX) view of cardiac magnetic resonance (CMR) is challenged by the sparse spatial structure of CMR. The strategy of multi-view CMR fusion can provide fine-grained spatial structure for accurate segmentation. However, the large information misalignment and lack of dense 3D CMR as fusion target in multi-view CMR fusion, and the different spatial resolution between the fusion result and the ground truth in segmentation limit the strategy. In this study, we propose a multi-view spatial-aware adversarial network (MVSGAN). It studies the perception of fine-grained cardiac structure for accurate segmentation by the spatialaware multi-view CMR fusion. It consists of three modules: (1) A residual adversarial fusion (RAF) module takes inter-slices deep correlation and anatomical prior to refine the spatial structures by residual supplement and adversarial optimization. (2) A structural perception-aggregation (SPA) module establishes the spatial correlation between the dense cardiac model and sparse label for accurate CMR LV myocardium segmentation. (3) A joint training strategy utilizes the dense SAX volume as explicit and implicit goals to jointly optimize the framework. The experiments are applied on a public dataset and a clinical dataset to evaluate the performance of MVSGAN. The average Dice and Jaccard score of LV myocardium segmentation obtained by MVSGAN are highest among seven existing state-of-the-art methods, which are up to 0. 92 and 0. 75. It is concluded that the spatial-aware multi-view CMR fusion can provide meaningful spatial correlation for accurate LV myocardium segmentation.

NeurIPS Conference 2022 Conference Paper

Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective

• Changyou Chen
• Jianyi Zhang
• Yi Xu
• Liqun Chen
• Jiali Duan
• Yiran Chen
• Son Tran
• Belinda Zeng

Contrastive learning (CL) has been the de facto technique for self-supervised representation learning (SSL), with impressive empirical success such as multi-modal representation learning. However, traditional CL loss only considers negative samples from a minibatch, which could cause biased gradients due to the non-decomposibility of the loss. For the first time, we consider optimizing a more generalized contrastive loss, where each data sample is associated with an infinite number of negative samples. We show that directly using minibatch stochastic optimization could lead to gradient bias. To remedy this, we propose an efficient Bayesian data augmentation technique to augment the contrastive loss into a decomposable one, where standard stochastic optimization can be directly applied without gradient bias. Specifically, our augmented loss defines a joint distribution over the model parameters and the augmented parameters, which can be conveniently optimized by a proposed stochastic expectation-maximization algorithm. Our framework is more general and is related to several popular SSL algorithms. We verify our framework on both small scale models and several large foundation models, including SSL of ImageNet and SSL for vision-language representation learning. Experiment results indicate the existence of gradient bias in all cases, and demonstrate the effectiveness of the proposed method on improving previous state of the arts. Remarkably, our method can outperform the strong MoCo-v3 under the same hyper-parameter setting with only around half of the minibatch size; and also obtains strong results in the recent public benchmark ELEVATER for few-shot image classification.

NeurIPS Conference 2021 Conference Paper

An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives

• Qi Qi
• Zhishuai Guo
• Yi Xu
• Rong Jin
• Tianbao Yang

In this paper, we propose a practical online method for solving a class of distributional robust optimization (DRO) with non-convex objectives, which has important applications in machine learning for improving the robustness of neural networks. In the literature, most methods for solving DRO are based on stochastic primal-dual methods. However, primal-dual methods for DRO suffer from several drawbacks: (1) manipulating a high-dimensional dual variable corresponding to the size of data is time expensive; (2) they are not friendly to online learning where data is coming sequentially. To address these issues, we consider a class of DRO with an KL divergence regularization on the dual variables, transform the min-max problem into a compositional minimization problem, and propose practical duality-free online stochastic methods without requiring a large mini-batch size. We establish the state-of-the-art complexities of the proposed methods with and without a Polyak-Łojasiewicz (PL) condition of the objective. Empirical studies on large-scale deep learning tasks (i) demonstrate that our method can speed up the training by more than 2 times than baseline methods and save days of training time on a large-scale dataset with ∼ 265K images, and (ii) verify the supreme performance of DRO over Empirical Risk Minimization (ERM) on imbalanced datasets. Of independent interest, the proposed method can be also used for solving a family of stochastic compositional problems with state-of-the-art complexities.

JBHI Journal 2021 Journal Article

Deep Adaptive Blending Network for 3D Magnetic Resonance Image Denoising

• Yi Xu
• Kang Han
• Yongming Zhou
• Jian Wu
• Xin Xie
• Wei Xiang

The visual quality of magnetic resonance images (MRIs) is crucial for clinical diagnosis and scientific research. The main source of quality degradation is the noise generated during MRI acquisition. Although denoising MRI by deep learning methods shows great superiority compared with traditional methods, the deep learning methods reported to date in the literature cannot simultaneously leverage long-range and hierarchical information, and cannot adequately utilize the similarity in 3D MRI. In this paper, we address the two issues by proposing a deep adaptive blending network (DABN) characterized by a large receptive field residual dense block and an adaptive blending method. We first propose the large receptive field residual dense block that can capture long-range information and fuse hierarchical features simultaneously. Then we propose the adaptive blending method that produces denoised pixels by adaptively filtering 3D MRI, which explicitly utilizes the similarity in 3D MRI. Residual is also considered as a compensating item after adaptive filtering. The blending adaptive filter and residual are predicted by a network consisting of several large receptive field residual dense blocks. Experimental results show that the proposed DABN outperforms state-of-the-art denoising methods in both clinical and simulated MRI data.

NeurIPS Conference 2021 Conference Paper

DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples

• Yi Xu
• Jiandong Ding
• Lu Zhang
• Shuigeng Zhou

The scarcity of labeled data is a critical obstacle to deep learning. Semi-supervised learning (SSL) provides a promising way to leverage unlabeled data by pseudo labels. However, when the size of labeled data is very small (say a few labeled samples per class), SSL performs poorly and unstably, possibly due to the low quality of learned pseudo labels. In this paper, we propose a new SSL method called DP-SSL that adopts an innovative data programming (DP) scheme to generate probabilistic labels for unlabeled data. Different from existing DP methods that rely on human experts to provide initial labeling functions (LFs), we develop a multiple-choice learning~(MCL) based approach to automatically generate LFs from scratch in SSL style. With the noisy labels produced by the LFs, we design a label model to resolve the conflict and overlap among the noisy labels, and finally infer probabilistic labels for unlabeled samples. Extensive experiments on four standard SSL benchmarks show that DP-SSL can provide reliable labels for unlabeled data and achieve better classification performance on test sets than existing SSL methods, especially when only a small number of labeled samples are available. Concretely, for CIFAR-10 with only 40 labeled samples, DP-SSL achieves 93. 82% annotation accuracy on unlabeled data and 93. 46% classification accuracy on test data, which are higher than the SOTA results.

AAAI Conference 2021 Conference Paper

GIF Thumbnails: Attract More Clicks to Your Videos

• Yi Xu
• Fan Bai
• Yingxuan Shi
• Qiuyu Chen
• Longwen Gao
• Kai Tian
• Shuigeng Zhou
• Huyang Sun

With the rapid increase of mobile devices and online media, more and more people prefer posting/viewing videos online. Generally, these videos are presented on video streaming sites with image thumbnails and text titles. While facing huge amounts of videos, a viewer clicks through a certain video with high probability because of its eye-catching thumbnail. However, current video thumbnails are created manually, which is time-consuming and quality-unguaranteed. And static image thumbnails contain very limited information of the corresponding videos, which prevents users from successfully clicking what they really want to view. In this paper, we address a novel problem, namely GIF thumbnail generation, which aims to automatically generate GIF thumbnails for videos and consequently boost their Click- Through-Rate (CTR). Here, a GIF thumbnail is an animated GIF file consisting of multiple segments from the video, containing more information of the target video than a static image thumbnail. To support this study, we build the first GIF thumbnails benchmark dataset that consists of 1070 videos covering a total duration of 69. 1 hours, and 5394 corresponding manually-annotated GIFs. To solve this problem, we propose a learning-based automatic GIF thumbnail generation model, which is called Generative Variational Dual- Encoder (GEVADEN). As not relying on any user interaction information (e. g. time-sync comments and real-time view counts), this model is applicable to newly-uploaded/rarelyviewed videos. Experiments on our built dataset show that GEVADEN significantly outperforms several baselines, including video-summarization and highlight-detection based ones. Furthermore, we develop a pilot application of the proposed model on an online video platform with 9814 videos covering 1231 hours, which shows that our model achieves a 37. 5% CTR improvement over traditional image thumbnails. This further validates the effectiveness of the proposed model and the promising application prospect of GIF thumbnails.

TIST Journal 2021 Journal Article

Simultaneous Past and Current Social Interaction-aware Trajectory Prediction for Multiple Intelligent Agents in Dynamic Scenes

• Yanliang Zhu
• Dongchun Ren
• Yi Xu
• Deheng Qian
• Mingyu Fan
• Xin Li
• Huaxia Xia

Trajectory prediction of multiple agents in a crowded scene is an essential component in many applications, including intelligent monitoring, autonomous robotics, and self-driving cars. Accurate agent trajectory prediction remains a significant challenge because of the complex dynamic interactions among the agents and between them and the surrounding scene. To address the challenge, we propose a decoupled attention-based spatial-temporal modeling strategy in the proposed trajectory prediction method. The past and current interactions among agents are dynamically and adaptively summarized by two separate attention-based networks and have proven powerful in improving the prediction accuracy. Moreover, it is optional in the proposed method to make use of the road map and the plan of the ego-agent for scene-compliant and accurate predictions. The road map feature is efficiently extracted by a convolutional neural network, and the features of the ego-agent’s plan is extracted by a gated recurrent network with an attention module based on the temporal characteristic. Experiments on benchmark trajectory prediction datasets demonstrate that the proposed method is effective when the ego-agent plan and the the surrounding scene information are provided and achieves state-of-the-art performance with only the observed trajectories.

AAAI Conference 2021 Conference Paper

Topic-Aware Multi-turn Dialogue Modeling

• Yi Xu
• Hai Zhao
• Zhuosheng Zhang

In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances. As a conversation goes on, topic shift at discourse-level naturally happens through the continuous multi-turn dialogue context. However, all known retrieval-based systems are satisfied with exploiting local topic words for context utterance representation but fail to capture such essential global topic-aware clues at discourse-level. Instead of taking topic-agnostic ngram utterance as processing unit for matching purpose in existing systems, this paper presents a novel topic-aware solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way, so that the resulted model is capable of capturing salient topic shift at discourse-level in need and thus effectively track topic flow during multi-turn conversation. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dualattention Matching (TADAM) Network, which matches each topic segment with the response in a dual cross-attention way. Experimental results on three public datasets show TADAM can outperform the state-of-the-art method, especially by 3. 3% on E-commerce dataset that has an obvious topic shift.

JBHI Journal 2021 Journal Article

Using BI-RADS Stratifications as Auxiliary Information for Breast Masses Classification in Ultrasound Images

• Jie Xing
• Chao Chen
• Qinyang Lu
• Xun Cai
• Aijun Yu
• Yi Xu
• Xiaoling Xia
• Yue Sun

Breast Ultrasound (BUS) imaging has been recognized as an essential imaging modality for breast masses classification in China. Current deep learning (DL) based solutions for BUS classification seek to feed ultrasound (US) images into deep convolutional neural networks (CNNs), to learn a hierarchical combination of features for discriminating malignant and benign masses. One existing problem in current DL-based BUS classification was the lack of spatial and channel-wise features weighting, which inevitably allow interference from redundant features and low sensitivity. In this study, we aim to incorporate the instructive information provided by breast imaging reporting and data system (BI-RADS) within DL-based classification. A novel DL-based BI-RADS Vector-Attention Network (BVA Net) that trains with both texture information and decoded information from BI-RADS stratifications was proposed for the task. Three baseline models, pre-trained DenseNet-121, ResNet-50 and Residual-Attention Network (RA Net) were included for comparison. Experiments were conducted on a large scale private main dataset and two public datasets, UDIAT and BUSI. On the main dataset, BVA Net outperformed other models, in terms of AUC (area under the receiver operating curve, 0. 908), ACC (accuracy, 0. 865), sensitivity (0. 812) and precision (0. 795). BVA Net also achieved the high AUC (0. 87 and 0. 882) and ACC (0. 859 and 0. 843), on UDIAT and BUSI. Moreover, we proposed a method that integrates both BVA Net binary classification and BI-RADS stratification estimation, called integrated classification. The introduction of integrated classification helped improving the overall sensitivity while maintaining a high specificity.

AAAI Conference 2020 Conference Paper

CF-LSTM: Cascaded Feature-Based Long Short-Term Networks for Predicting Pedestrian Trajectory

• Yi Xu
• Jing Yang
• Shaoyi Du

Pedestrian trajectory prediction is an important but difficult task in self-driving or autonomous mobile robot field because there are complex unpredictable human-human interactions in crowded scenarios. There have been a large number of studies that attempt to understand humans’ social behavior. However, most of these studies extract location features from previous one time step while neglecting the vital velocity features. In order to address this issue, we propose a novel feature-cascaded framework for long short-term network (CF-LSTM) without extra artificial settings or social rules. In this framework, feature information from previous two time steps are firstly extracted and then integrated as a cascaded feature to LSTM, which is able to capture the previous location information and dynamic velocity information, simultaneously. In addition, this scene-agnostic cascaded feature is the external manifestation of complex human-human interactions, which can also effectively capture dynamic interaction information in different scenes without any other pedestrians’ information. Experiments on public benchmark datasets indicate that our model achieves better performance than the state-of-the-art methods and this feature-cascaded framework has the ability to implicitly learn human-human interactions.

AAAI Conference 2020 Conference Paper

Network as Regularization for Training Deep Neural Networks: Framework, Model and Performance

• Kai Tian
• Yi Xu
• Jihong Guan
• Shuigeng Zhou

Despite powerful representation ability, deep neural networks (DNNs) are prone to over-fitting, because of overparametrization. Existing works have explored various regularization techniques to tackle the over-fitting problem. Some of them employed soft targets rather than one-hot labels to guide network training (e. g. label smoothing in classification tasks), which are called target-based regularization approaches in this paper. To alleviate the over-fitting problem, here we propose a new and general regularization framework that introduces an auxiliary network to dynamically incorporate guided semantic disturbance to the labels. We call it Network as Regularization (NaR in short). During training, the disturbance is constructed by a convex combination of the predictions of the target network and the auxiliary network. These two networks are initialized separately. And the auxiliary network is trained independently from the target network, while providing instance-level and class-level semantic information to the latter progressively. We conduct extensive experiments to validate the effectiveness of the proposed method. Experimental results show that NaR outperforms many state-of-the-art target-based regularization methods, and other regularization approaches (e. g. mixup) can also benefit from combining with NaR.

NeurIPS Conference 2020 Conference Paper

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization

• Yan Yan
• Yi Xu
• Qihang Lin
• Wei Liu
• Tianbao Yang

Epoch gradient descent method (a. k. a. Epoch-GD) proposed by (Hazan and Kale, 2011) was deemeda breakthrough for stochastic strongly convex minimization, which achieves theoptimal convergence rate of O(1/T) with T iterative updates for the objective gap. However, its extension to solving stochastic min-max problems with strong convexity and strong concavity still remains open, and it is still unclear whethera fast rate ofO(1/T)for theduality gapis achievable for stochastic min-max optimization under strong convexity and strong concavity. Although some re-cent studies have proposed stochastic algorithms with fast convergence rates formin-max problems, they require additional assumptions about the problem, e. g. ,smoothness, bi-linear structure, etc. In this paper, we bridge this gap by providinga sharp analysis of epoch-wise stochastic gradient descent ascent method (referredto as Epoch-GDA) for solving strongly convex strongly concave (SCSC) min-maxproblems, without imposing any additional assumption about smoothness or the function’s structure. To the best of our knowledge, our result is the first one that shows Epoch-GDA can achieve the optimal rate ofO(1/T)for the duality gapof general SCSC min-max problems. We emphasize that such generalization of Epoch-GD for strongly convex minimization problems to Epoch-GDA for SCSC min-max problems is non-trivial and requires novel technical analysis. Moreover, we notice that the key lemma can also be used for proving the convergence of Epoch-GDA for weakly-convex strongly-concave min-max problems, leading to a nearly optimal complexity without resorting to smoothness or other structural conditions.

AAMAS Conference 2019 Conference Paper

Facility Location Games with Externalities

• Minming Li
• Lili Mei
• Yi Xu
• Guochuan Zhang
• Yingchao Zhao

Facility location games study the scenario where a facility is to be placed based on the reported information from agents. In the society where there are relationships between agents, it is quite natural that one agent’s gain will affect other agents’ gain (either increase for a collaborator or decrease for a competitor). By using externality to represent this type of agent interaction, for the first time we introduce it into the facility location games in this paper. Namely, we study the extension where agents’ utilities will be affected by other agents. We derive necessary and sufficient conditions for well known existing mechanisms and also prove strong lower bounds.

NeurIPS Conference 2019 Conference Paper

Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems

• Yi Xu
• Rong Jin
• Tianbao Yang

Stochastic Proximal Gradient (SPG) methods have been widely used for solving optimization problems with a simple (possibly non-smooth) regularizer in machine learning and statistics. However, to the best of our knowledge no non-asymptotic convergence analysis of SPG exists for non-convex optimization with a non-smooth and non-convex regularizer. All existing non-asymptotic analysis of SPG for solving non-smooth non-convex problems require the non-smooth regularizer to be a convex function, and hence are not applicable to a non-smooth non-convex regularized problem. This work initiates the analysis to bridge this gap and opens the door to non-asymptotic convergence analysis of non-smooth non-convex regularized problems. We analyze several variants of mini-batch SPG methods for minimizing a non-convex objective that consists of a smooth non-convex loss and a non-smooth non-convex regularizer. Our contributions are two-fold: (i) we show that they enjoy the same complexities as their counterparts for solving convex regularized non-convex problems in terms of finding an approximate stationary point; (ii) we develop more practical variants using dynamic mini-batch size instead of a fixed mini-batch size without requiring the target accuracy level of solution. The significance of our results is that they improve upon the-state-of-art results for solving non-smooth non-convex regularized problems. We also empirically demonstrate the effectiveness of the considered SPG methods in comparison with other peer stochastic methods.

IJCAI Conference 2019 Conference Paper

On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization

• Yi Xu
• Zhuoning Yuan
• Sen Yang
• Rong Jin
• Tianbao Yang

Extrapolation is a well-known technique for solving convex optimization and variational inequalities and recently attracts some attention for non-convex optimization. Several recent works have empirically shown its success in some machine learning tasks. However, it has not been analyzed for non-convex minimization and there still remains a gap between the theory and the practice. In this paper, we analyze gradient descent and stochastic gradient descent with extrapolation for finding an approximate first-order stationary point in smooth non-convex optimization problems. Our convergence upper bounds show that the algorithms with extrapolation can be accelerated than without extrapolation.

TCS Journal 2019 Journal Article

The discrete and mixed minimax 2-center problems

• Yi Xu
• Jigen Peng
• Yinfeng Xu
• Binhai Zhu

Letting P be a set of n points in the plane, the discrete minimax 2-center problem ( D M M 2 C P ) is to find two disks centered at { p 1, p 2 } ∈ P that minimize the maximum of two terms, namely, the Euclidean distance between two centers and the distance of any other point to the closer center. The mixed minimax 2-center problem ( M M M 2 C P ) is when one of the two centers is not in P. We present algorithms solving the D M M 2 C P and M M M 2 C P. The time complexities of solving the D M M 2 C P and M M M 2 C P are O ( n 2 log ⁡ n ) and O ( n 2 log 2 ⁡ n ) respectively. Furthermore, we consider two Steiner minimum sum dipolar spanning tree problems, in which one of the two dipoles is a Steiner point and the dipoles are both Steiner points. These two problems are shown to be solvable in O ( n log ⁡ n ) and O ( n ) time respectively.

NeurIPS Conference 2018 Conference Paper

First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time

• Yi Xu
• Rong Jin
• Tianbao Yang

(This is a theory paper) In this paper, we consider first-order methods for solving stochastic non-convex optimization problems. The key building block of the proposed algorithms is first-order procedures to extract negative curvature from the Hessian matrix through a principled sequence starting from noise, which are referred to {\it NEgative-curvature-Originated-from-Noise or NEON} and are of independent interest. Based on this building block, we design purely first-order stochastic algorithms for escaping from non-degenerate saddle points with a much better time complexity (almost linear time in the problem's dimensionality). In particular, we develop a general framework of {\it first-order stochastic algorithms} with a second-order convergence guarantee based on our new technique and existing algorithms that may only converge to a first-order stationary point. For finding a nearly {\it second-order stationary point} $\x$ such that $\|\nabla F(\x)\|\leq \epsilon$ and $\nabla^2 F(\x)\geq -\sqrt{\epsilon}I$ (in high probability), the best time complexity of the presented algorithms is $\widetilde O(d/\epsilon^{3. 5})$, where $F(\cdot)$ denotes the objective function and $d$ is the dimensionality of the problem. To the best of our knowledge, this is the first theoretical result of first-order stochastic algorithms with an almost linear time in terms of problem's dimensionality for finding second-order stationary points, which is even competitive with existing stochastic algorithms hinging on the second-order information.

YNICL Journal 2018 Journal Article

Neurotransmitter alterations in the anterior cingulate cortex in Crohn's disease patients with abdominal pain: A preliminary MR spectroscopy study

• Kun Lv
• Wenwen Song
• Rui Tang
• Zhiyong Pan
• Yong Zhang
• Yi Xu
• Bin Lv
• Yihong Fan

PURPOSE: H-MRS) to further explore the neural mechanism. METHODS: Sixteen CD patients with abdominal pain and 13 CD patients without abdominal pain, were recruited alongside 20 healthy controls (HCs) for this study. Clinical evaluations, including the 0-10 Visual Analogue Scale (VAS) of pain, Hospital Anxiety and Depression Scale (HADS) and Crohn's Disease Activity Index (CDAI), were evaluated prior to MR scanning. This study selected the bilateral ACC as the region of interest (ROI). The metabolites of the bilateral ACC were quantitatively analyzed by LCModel and Gannet. A independent sample t-test and one-way analysis of variance (ANOVA) were performed for statistical analysis. Spearman correlation analyses were performed to examine the relationship between the metabolite levels and clinical evaluations. RESULTS: The results indicated that CD patients with abdominal pain exhibited significantly higher levels of Glutamate (Glu)/(creatine + phosphocreatine, total creatine, tCr) over CD patients without abdominal pain, and HCs (p = 0.003, 0.009, respectively) in the bilateral ACC. The level of (Glutamate + Glutamine, Glx)/tCr of pain CD group was higher than non-pain CD group (p = 0.022). Moreover, within the pain CD group, Glu/tCr and Glx/tCr levels correlated strongly with the VAS scores of pain (ρ = 0.86, 0.59 respectively, p < 0.05). Meanwhile, the results indicates that CD patients with abdominal pain have significantly lower levels of γ-aminobutyric acid plus (GABA+)/tCr (p = 0.002) than HCs. To some extent, CDAI demonstrated a trend of negative correlation with GABA+/tCr levels (p = 0.088, ρ = -0.60). CONCLUSION: The neural mechanism of CD patients with abdominal pain in pain processing is tightly associated with neurochemical metabolites. An imbalance in Glu and GABA may play a key role in abdominal pain processing for patients with CD. This mechanism of pain may associate with the intestinal microbiota on the brain-gut axis.

NeurIPS Conference 2017 Conference Paper

Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter

• Yi Xu
• Qihang Lin
• Tianbao Yang

Error bound, an inherent property of an optimization problem, has recently revived in the development of algorithms with improved global convergence without strong convexity. The most studied error bound is the quadratic error bound, which generalizes strong convexity and is satisfied by a large family of machine learning problems. Quadratic error bound have been leveraged to achieve linear convergence in many first-order methods including the stochastic variance reduced gradient (SVRG) method, which is one of the most important stochastic optimization methods in machine learning. However, the studies along this direction face the critical issue that the algorithms must depend on an unknown growth parameter (a generalization of strong convexity modulus) in the error bound. This parameter is difficult to estimate exactly and the algorithms choosing this parameter heuristically do not have theoretical convergence guarantee. To address this issue, we propose novel SVRG methods that automatically search for this unknown parameter on the fly of optimization while still obtain almost the same convergence rate as when this parameter is known. We also analyze the convergence property of SVRG methods under H\"{o}lderian error bound, which generalizes the quadratic error bound.

NeurIPS Conference 2017 Conference Paper

ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization

• Yi Xu
• Mingrui Liu
• Qihang Lin
• Tianbao Yang

Alternating direction method of multipliers (ADMM) has received tremendous interest for solving numerous problems in machine learning, statistics and signal processing. However, it is known that the performance of ADMM and many of its variants is very sensitive to the penalty parameter of a quadratic penalty applied to the equality constraints. Although several approaches have been proposed for dynamically changing this parameter during the course of optimization, they do not yield theoretical improvement in the convergence rate and are not directly applicable to stochastic ADMM. In this paper, we develop a new ADMM and its linearized variant with a new adaptive scheme to update the penalty parameter. Our methods can be applied under both deterministic and stochastic optimization settings for structured non-smooth objective function. The novelty of the proposed scheme lies at that it is adaptive to a local sharpness property of the objective function, which marks the key difference from previous adaptive scheme that adjusts the penalty parameter per-iteration based on certain conditions on iterates. On theoretical side, given the local sharpness characterized by an exponent $\theta\in(0, 1]$, we show that the proposed ADMM enjoys an improved iteration complexity of $\widetilde O(1/\epsilon^{1-\theta})$\footnote{$\widetilde O()$ suppresses a logarithmic factor. } in the deterministic setting and an iteration complexity of $\widetilde O(1/\epsilon^{2(1-\theta)})$ in the stochastic setting without smoothness and strong convexity assumptions. The complexity in either setting improves that of the standard ADMM which only uses a fixed penalty parameter. On the practical side, we demonstrate that the proposed algorithms converge comparably to, if not much faster than, ADMM with a fine-tuned fixed penalty parameter.

AAAI Conference 2017 Conference Paper

Efficient Non-Oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee

• Yi Xu
• Haiqin Yang
• Lijun Zhang
• Tianbao Yang

In this paper, we address learning problems for high dimensional data. Previously, oblivious random projection based approaches that project high dimensional features onto a random subspace have been used in practice for tackling highdimensionality challenge in machine learning. Recently, various non-oblivious randomized reduction methods have been developed and deployed for solving many numerical problems such as matrix product approximation, low-rank matrix approximation, etc. However, they are less explored for the machine learning tasks, e. g. , classification. More seriously, the theoretical analysis of excess risk bounds for risk minimization, an important measure of generalization performance, has not been established for non-oblivious randomized reduction methods. It therefore remains an open problem what is the benefit of using them over previous oblivious random projection based approaches. To tackle these challenges, we propose an algorithmic framework for employing non-oblivious randomized reduction method for general empirical risk minimizing in machine learning tasks, where the original high-dimensional features are projected onto a random subspace that is derived from the data with a small matrix approximation error. We then derive the first excess risk bound for the proposed non-oblivious randomized reduction approach without requiring strong assumptions on the training data. The established excess risk bound exhibits that the proposed approach provides much better generalization performance and it also sheds more insights about different randomized reduction approaches. Finally, we conduct extensive experiments on both synthetic and real-world benchmark datasets, whose dimension scales to O(107 ), to demonstrate the efficacy of our proposed approach.

ICRA Conference 2017 Conference Paper

Grasp quality evaluation and planning for objects with negative curvature

• Shuo Liu 0006
• Zhe Hu
• Hao Zhang
• Mingu Kwon
• Zhikang Wang
• Yi Xu
• Stefano Carpin

We consider the problem of grasping concave objects, i. e. , objects whose surface includes regions with negative curvature. When a multifingered hand is used to restrain these objects, these areas can be advantageously used to determine grasps capable of more robustly resisting to external disturbance wrenches. We propose a new grasp quality metric specifically suited for this case, and we use it to inform a grasp planner searching the space of possible grasps. Our findings are validated both in simulation and on a real robot system executing a bin picking task. Experimental validation shows that our method is more effective than those not explicitly considering negative curvature.

NeurIPS Conference 2016 Conference Paper

Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$

• Yi Xu
• Yan Yan
• Qihang Lin
• Tianbao Yang

In this paper, we develop a novel {\bf ho}moto{\bf p}y {\bf s}moothing (HOPS) algorithm for solving a family of non-smooth problems that is composed of a non-smooth term with an explicit max-structure and a smooth term or a simple non-smooth term whose proximal mapping is easy to compute. The best known iteration complexity for solving such non-smooth optimization problems is $O(1/\epsilon)$ without any assumption on the strong convexity. In this work, we will show that the proposed HOPS achieved a lower iteration complexity of $\tilde O(1/\epsilon^{1-\theta})$ with $\theta\in(0, 1]$ capturing the local sharpness of the objective function around the optimal solutions. To the best of our knowledge, this is the lowest iteration complexity achieved so far for the considered non-smooth optimization problems without strong convexity assumption. The HOPS algorithm employs Nesterov's smoothing technique and Nesterov's accelerated gradient method and runs in stages, which gradually decreases the smoothing parameter in a stage-wise manner until it yields a sufficiently good approximation of the original function. We show that HOPS enjoys a linear convergence for many well-known non-smooth problems (e. g. , empirical risk minimization with a piece-wise linear loss function and $\ell_1$ norm regularizer, finding a point in a polyhedron, cone programming, etc). Experimental results verify the effectiveness of HOPS in comparison with Nesterov's smoothing algorithm and the primal-dual style of first-order methods.

AAAI Conference 2015 Conference Paper

Dictionary Learning with Mutually Reinforcing Group-Graph Structures

• Hongteng Xu
• Licheng Yu
• Dixin Luo
• Hongyuan Zha
• Yi Xu

In this paper, we propose a novel dictionary learning method in the semi-supervised setting by dynamically coupling graph and group structures. To this end, samples are represented by sparse codes inheriting their graph structure while the labeled samples within the same class are represented with group sparsity, sharing the same atoms of the dictionary. Instead of statically combining graph and group structures, we take advantage of them in a mutually reinforcing way — in the dictionary learning phase, we introduce the unlabeled samples into groups by an entropy-based method and then update the corresponding local graph, resulting in a more structured and discriminative dictionary. We analyze the relationship between the two structures and prove the convergence of our proposed method. Focusing on image classification task, we evaluate our approach on several datasets and obtain superior performance compared with the state-of-the-art methods, especially in the case of only a few labeled samples and limited dictionary size.

ICRA Conference 2014 Conference Paper

A vision-guided robot manipulator for surgical instrument singulation in a cluttered environment

• Yi Xu
• Xianqiao Tong
• Ying Mao 0006
• Weston B. Griffin
• Balajee Kannan
• Lynn A. DeRose

The logistics of counting, sorting, sterilizing, and transporting surgical instruments is labor and capital intensive. Furthermore, infection due to improper sterilization is a critical safety hazard. To address these problems, we have developed a unique robotic manipulation system that is capable of accurately singulating surgical instruments in a cluttered environment. Our solution is comprised of two parts. First, we use a single-view vision algorithm for identifying surgical instruments from a pile and estimating their poses. Occlusion reasoning is performed to determine the next instrument to grip using a contrast invariant feature descriptor. Second, we design a compliant electromagnetic gripper that is capable of picking up the identified surgical instrument based on its estimated pose. We validate our solution through instrument singulation experiments demonstrating identification, localization accuracy, and robustness of occlusion reasoning as well as the flexibility of the electromagnetic gripper.

YNIMG Journal 2009 Journal Article

Neural Crrelates of Disgust Induced by Sexual Stimuli in Homosexual and Heterosexual men: a fMRI study

• S-H Hu
• M-M Zhang
• L-J Xu
• N-W Wei
• J-B Hu
• Yi Xu