Author name cluster

Xinyu Liu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

22 papers

2 author rows

EAAI Journal 2026 Journal Article

An improved retrieval-augmented long-term grouting power prediction method: Rejecting low-similarity retrievals

Baoxi Liu
Liangsi Xu
Bingyu Ren
Chengyu Yu
Hongling Yu
Xiangyu Chen
Xinyu Liu

Grouting power long-term prediction is beneficial to regulating power output. Traditional long-term prediction methods require iterative updates with newly accumulated data during construction, which is time-consuming. Retrieval-augmented methods not only achieve higher prediction accuracy but also enable more efficient performance upgrades through database updates, avoiding the need to retrain models. However, conventional retrieval augmented frameworks unconditionally incorporate retrieved sequences into the prediction process, even when their similarity to the query is low. This design choice can introduce noisy or irrelevant historical patterns, misleading the fusion mechanism and degrading overall performance. To address this issue, this study proposes a retrieval-augmented method for long-term grouting power prediction with a rejection-substitution mechanism. Compared with the naive retrieval augmented prediction method, this mechanism enables selective fusion of retrievals by evaluating the similarity of each retrieved sequence before integration. If the similarity falls below a predefined threshold, the corresponding result is substituted with a prediction from the TimeXer model. Otherwise, the retrieved result is retained. The processed results are then fused by a Gate Recurrent Unit network to generate the final prediction. To validate the effectiveness of the proposed method, experiments were conducted on both a grouting power dataset and a publicly accessible dataset. The results indicate that incorporating a rejection-substitution mechanism enhances the prediction accuracy compared to the traditional retrieval-augmented prediction approach.

Details DOI

AAAI Conference 2026 Conference Paper

CreBench: Human-Aligned Creativity Evaluation from Idea to Process to Product

Kaiwen Xue
Chenglong Li
Zhonghong Ou
Guoxin Zhang
Kaoyan Lu
Shuai Lyu
Yifan Zhu
Ping Zong

Human-defined creativity is highly abstract, posing a challenge for multimodal large language models (MLLMs) to comprehend and assess creativity that aligns with human judgments. The absence of an existing benchmark further exacerbates this dilemma. To this end, we propose CreBench, which consists of two key components: 1) an evaluation benchmark covering the multiple dimensions from creative idea to process to products; 2) CreMIT (Creativity Multimodal Instruction Tuning dataset), a multimodal creativity evaluation dataset, consisting of 2.2K diverse-sourced multimodal data, 79.2K human feedbacks and 4.7M multityped instructions. Specifically, to ensure MLLMs can handle diverse creativity-related queries, we prompt GPT to refine the human feedback to activate stronger creativity assessment capabilities. CreBench serves as a foundation for building MLLMs that understand human-aligned creativity. Based on the CreBench, we fine-tune open-source general MLLMs, resulting in CreExpert, a multimodal creativity evaluation expert model. Extensive experiments demonstrate that the proposed CreExpert models achieve significantly better alignment with human creativity evaluation compared to state-ofthe-art MLLMs, including the most advanced GPT-4V and Gemini-Pro-Vision.

PDF Details DOI

AAAI Conference 2026 Conference Paper

Learning Latent Imaging Biomarkers for Interpretable Microvascular Invasion Prediction in Hepatocellular Carcinoma

Ji Rao
Xinyu Liu
Yong Yi
Ying Xiao
Ye Luo

Microvascular invasion (MVI) is a critical prognostic factor that significantly impacts postoperative outcomes in hepatocellular carcinoma (HCC). As the current gold standard for the diagnosis of MVI is based on the postoperative histopathological examination of whole slide images, accurate preoperative prediction of MVI status using magnetic resonance imaging (MRI) presents both a substantial clinical imperative and a significant challenge. In order to discover reliable MRI-based imaging biomarkers to support clinical decision making and enhance the interpretability of deep learning-based diagnostic models, we propose a novel interpretable MVI prediction framework in which the shared latent visual attributes are first learned and then used for potential imaging biomarker extraction and MVI diagnosis, respectively. To ensure that the visual attributes of these biomarkers are generalizable across diverse patients, the similarity constraints at the intra-patient level and the inter-patient level are enforced within the learned feature space, enabling intuitive biomarker discovery directly from the original image space. To guarantee semantic alignment between biomarkers and the characteristics of individual patients, we introduce a novel classification mechanism that directly links the alignment between each biomarker and patient-specific characteristics with the prediction, thereby ensuring a precise prediction of MVI. Furthermore, the interpretability of the model is enhanced by integrating a mask-based visual explanation method that highlights regions in patient images that correspond to the identified biomarkers. Extensive experiments on two MVI prediction datasets: HCC-WCH and HCC-ZSH unequivocally demonstrate our method's superior performance in both classification accuracy and interpretability.

PDF Details DOI

TMLR Journal 2026 Journal Article

OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control

Darryl C. Jacob
Xinyu Liu
Muchao Ye
Xiaoyong Yuan
Pan He

Transparent decision-making is essential for traffic signal control (TSC) systems to earn public trust. However, traditional reinforcement learning–based TSC methods function as black boxes, providing little to no insight into their decisions. Although large language models (LLMs) could provide the needed interpretability through natural language reasoning, they face challenges such as limited memory and difficulty in deriving optimal policies from sparse environmental feedback. Existing TSC methods that apply reinforcement fine-tuning to LLMs face notable training instability and deliver only limited improvements over pretrained models. We attribute this instability to the long-horizon nature of TSC: feedback is sparse and delayed, most control actions yield only marginal changes in congestion metrics, and the resulting weak reward signals interact poorly with policy-gradient optimization. We introduce OracleTSC, which addresses these issues through: (1) a reward hurdle mechanism that filters weak learning signals by subtracting a calibrated threshold from environmental feedback, and (2) preventing policy degeneracy by maximizing the probability of the chosen answer, which promotes consistent decision-making across multiple responses. Experiments on the standard LibSignal benchmark demonstrate that our approach enables a compact model (LLaMA3-8B) to achieve substantial improvements in traffic flow, with a $75%$ reduction in travel time and $67%$ decrease in queue lengths over the pretrained baseline while preserving interpretability through natural language explanations. Furthermore, the method exhibits strong cross-intersection generalization: a policy trained on one intersection transfers to a structurally distinct intersection with $17%$ lower travel time and $39%$ lower queue length, all without any additional finetuning for the target topology. These findings show that uncertainty-aware reward shaping could stabilize reinforcement fine-tuning and provide a new perspective for improving its effectiveness in TSC tasks.

PDF Details

AAAI Conference 2026 System Paper

PHOTONS: Pose-Free Human-Centric Photo-Realistic Real-Time Novel View Synthesis from Sparse Views

Yongyang Cheng
Boqin Qin
Zhao Hui
Xu Chen
Tao Zhang
Shang Sun
Haiquan Kang
Xiaojie Xu

We present PHOTONS (Pose-Free Human-Centric Photo-Realistic Real-Time Novel View Synthesis from Sparse Views), a real-time framework for novel view synthesis without requiring camera calibration. Our method reconstructs consistent 3D Gaussian point clouds and synthesizes 2K photo-realistic novel views from arbitrary numbers (>=2) of freely placed cameras. PHOTONS faithfully renders dynamic human bodies amid complex backgrounds, including interactive object manipulation and fine-grained details (e.g., hair strands), while maintaining 25 FPS throughput on commodity GPU like NVIDIA RTX 4090. By combining pose-free spatial point cloud reconstruction with Gaussian parameter estimation, our method demonstrates strong resilience to occlusions and camera perturbations. Additionally, we develop a 3D stereo system that drastically reduces setup complexity compared to existing solutions. Experiments on public and custom datasets show that PHOTONS outperforms state-of-the-art methods in both efficiency and visual quality.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

ContextAware: A Multi-Agent Framework for Detecting Harmful Image-Based Comments on Social Media

Zheng Wei
Mingchen Li
Pu Zhang
Xinyu Liu
Huamin Qu
Pan Hui

Detecting hidden stigmatization in social media poses significant challenges due to semantic misalignments between textual and visual modalities, as well as the subtlety of implicit stigmatization. Traditional approaches often fail to capture these complexities in real-world, multimodal content. To address this gap, we introduce ContextAware, an agent-based framework that leverages specialized modules to collaboratively process and analyze images, textual context, and social interactions. Our approach begins by clustering image embeddings to identify recurring content, activating high-likes agents for deeper analysis of images receiving substantial user engagement, while comprehensive agents handle lower-engagement images. By integrating case-based learning, textual sentiment, and vision-language models (VLMs), ContextAware refines its detection of harmful content. We evaluate ContextAware on a self-collected Douyin dataset focused on interracial relationships, comprising 871 short videos and 885, 502 comments—of which a notable portion are image-based. Experimental results show that ContextAware not only outperforms state-of-the-art methods in accuracy and F1 score but also effectively detects implicit stigmatization within the highly contextual environment of social media. Our findings underscore the importance of agent-based architectures and multimodal alignment in capturing nuanced, culturally specific forms of harmful content.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

ExAct: A Video-Language Benchmark for Expert Action Analysis

Han Yi
Yulu Pan
Feihong He
Xinyu Liu
Benjamin Zhang
Oluwatumininu Oguntola
Gedas Bertasius

We present ExAct, a new video-language benchmark for expert-level understanding of skilled physical human activities. Our new benchmark contains 3, 521 expert-curated video question-answer pairs spanning 11 physical activities in 6 domains: Sports, Bike Repair, Cooking, Health, Music, and Dance. ExAct requires the correct answer to be selected from five carefully designed candidate options, thus necessitating a nuanced, fine-grained, expert-level understanding of physical human skills. Evaluating the recent state-of-the-art VLMs on ExAct reveals a substantial performance gap relative to human expert performance. Specifically, the best-performing Gemini 2. 5 Pro model achieves only 55. 35% accuracy, well below the 82. 02% attained by trained human experts. We believe that ExAct will be beneficial for developing and evaluating VLMs capable of precise understanding of human skills in various physical and procedural domains. Dataset and code are available at https: //texaser. github. io/exact project page/.

PDF Details

NeurIPS Conference 2025 Conference Paper

Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features

Zixuan Xie
Xinyu Liu
Rohan Chandra
Shangtong Zhang

Linear TD($\lambda$) is one of the most fundamental reinforcement learning algorithms for policy evaluation. Previously, convergence rates are typically established under the assumption of linearly independent features, which does not hold in many practical scenarios. This paper instead establishes the first $L^2$ convergence rates for linear TD($\lambda$) operating under arbitrary features, without making any algorithmic modification or additional assumptions. Our results apply to both the discounted and average-reward settings. To address the potential non-uniqueness of solutions resulting from arbitrary features, we develop a novel stochastic approximation result featuring convergence rates to the solution set instead of a single point.

PDF Details

ICML Conference 2025 Conference Paper

Linear Q-Learning Does Not Diverge in L2: Convergence Rates to a Bounded Set

Xinyu Liu
Zixuan Xie
Shangtong Zhang

$Q$-learning is one of the most fundamental reinforcement learning algorithms. It is widely believed that $Q$-learning with linear function approximation (i. e. , linear $Q$-learning) suffers from possible divergence until the recent work Meyn (2024) which establishes the ultimate almost sure boundedness of the iterates of linear $Q$-learning. Building on this success, this paper further establishes the first $L^2$ convergence rate of linear $Q$-learning iterates (to a bounded set). Similar to Meyn (2024), we do not make any modification to the original linear $Q$-learning algorithm, do not make any Bellman completeness assumption, and do not make any near-optimality assumption on the behavior policy. All we need is an $\epsilon$-softmax behavior policy with an adaptive temperature. The key to our analysis is the general result of stochastic approximations under Markovian noise with fast-changing transition functions. As a side product, we also use this general result to establish the $L^2$ convergence rate of tabular $Q$-learning with an $\epsilon$-softmax behavior policy, for which we rely on a novel pseudo-contraction property of the weighted Bellman optimality operator.

Details

AAAI Conference 2025 Conference Paper

LiON: Learning Point-Wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data

Shaocong Xu
Pengfei Li
Qianpu Sun
Xinyu Liu
Yang Li
Shihui Guo
Zhen Wang
Bo Jiang

LiDAR-based semantic scene understanding is an important module in the modern autonomous driving perception stack. However, identifying outlier points in a LiDAR point cloud is challenging as LiDAR point clouds lack semantically-rich information. While former SOTA methods adopt heuristic architectures, we revisit this problem from the perspective of Selective Classification, which introduces a selective function into the standard closed-set classification setup. Our solution is built upon the basic idea of abstaining from choosing any inlier categories but learns a point-wise abstaining penalty with a margin-based loss. Apart from learning paradigms, synthesizing outliers to approximate unlimited real outliers is also critical, so we propose a strong synthesis pipeline that generates outliers originated from various factors: object categories, sampling patterns and sizes. We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance. We benchmark our method on SemanticKITTI and nuScenes and achieve SOTA results.

PDF Details DOI

AAAI Conference 2025 Conference Paper

SMR-Net: Semantic-Guided Mutually Reinforcing Network for Cross-Modal Image Fusion and Salient Object Detection

Guobao Xiao
Xinyu Liu
Zebin Lin
Rui Ming

This paper introduces a lightweight Semantic-guided Mutually Reinforcing network (SMR-Net) for the tasks of cross-modal image fusion and salient object detection (SOD). The core concept of SMR-Net is to leverage semantics for directing the mutual reinforcing between image fusion and SOD. Specifically, a Progressive Cross-modal Interaction (PCI) image fusion subnetwork is designed to exploit local interactions via convolution operations and extend to global interactions utilizing spatial and channel attention mechanisms. Subsequently, a cross-modal Bit-Plane Slicing-based SOD subnetwork (BPS) is developed by incorporating the fused image as a third modality. This component employs bit-plane slicing and the deformable convolution technique to effectively extract irregular semantic information embedded in fusion features. The refined semantic information then guides the feature extraction process of the source modalities in a reweighted fashion. By cascading these two subnetworks, BPS leverages final semantic results to direct PCI towards focusing more on semantic information. Ultimately, through this semantic-guided mutual enhancement process, SMR-Net excels in both producing high-quality fused images and achieving effective salient object detection. Our extensive experiments on image fusion and SOD tasks convincingly demonstrate the superiority of our network over existing state-of-the-art alternatives without introducing noticeable computational costs. Compared to nearest competitors, our method demonstrates a stronger generalization ability with 26% fewer parameters.

PDF Details DOI

JBHI Journal 2025 Journal Article

TSP-OCS: A Time-Series Prediction for Optimal Camera Selection in Multi-Viewpoint Surgical Video Analysis

Xinyu Liu
Xiaoguang Lin
Xiang Liu
Yong Yang
Hongqian Wang
Qilong Sun

Recording open surgery procedures is essential for educational and clinical evaluation purposes; however, traditional single-camera methods often face challenges such as occlusions caused by the surgeon's head and body, as well as limitations due to fixed camera angles, which undermine the comprehensibility of the recorded surgical content. In this study, we specifically focus on open thyroidectomy and employ a multi-viewpoint camera recording setup, in which six synchronized cameras capture the surgery from different angles simultaneously. We develop a supervised time-series prediction framework to automatically select the most informative camera views, ensuring better coverage of critical steps. Our model forecasts camera selections by extracting and fusing visual and semantic features from thyroidectomy videos using pre-trained models, followed by temporal modeling with TimeBlocks. We constructed a dataset of five thyroidectomy procedures with synchronized six-view recordings and conducted experiments. The results show that our method achieves stable accuracy compared with existing baselines and outperforms several mainstream time-series prediction models in this specific surgical scenario. This work provides an initial exploration of multi-view camera selection for thyroidectomy, with potential value for surgical video documentation and training. Code is available at https://github.com/Aveouter/SurgicalCamSwitch.

Details DOI

AAAI Conference 2025 Conference Paper

U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

Chenxin Li
Xinyu Liu
Wuyang Li
Cheng Wang
Hengyu Liu
Yifan Liu
Zhen Chen
Yixuan Yuan

U-Net has become a cornerstone in various visual applications such as image segmentation and diffusion probability models. While numerous innovative designs and improvements have been introduced by incorporating transformers or MLPs, the networks are still limited to linearly modeling patterns as well as the deficient interpretability. To address these challenges, our intuition is inspired by the impressive results of the Kolmogorov-Arnold Networks (KANs) in terms of accuracy and interpretability, which reshape the neural network learning via the stack of non-linear learnable activation functions derived from the Kolmogorov-Anold representation theorem. Specifically, in this paper, we explore the untapped potential of KANs in improving backbones for vision tasks. We investigate, modify and re-design the established U-Net pipeline by integrating the dedicated KAN layers on the tokenized intermediate representation, termed U-KAN. Rigorous medical image segmentation benchmarks verify the superiority of UKAN by higher accuracy even with less computation cost. We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures.

PDF Details DOI

EAAI Journal 2024 Journal Article

DIMN: Dual Integrated Matching Network for multi-choice reading comprehension

Qiang Wei
Kun Ma
Xinyu Liu
Ke Ji
Bo Yang
Ajith Abraham

Multi-choice reading comprehension is a task that involves selecting the correct option from a set of option choices. Recently, the attention mechanism has been widely used to acquire embedding representations. However, there are two significant challenges: (1) generating the contextualized representations, namely, drawing associated information, and (2) capturing the global interactive relationship, namely, drawing local semantics. To address these issues, we have proposed the Dual Integrated Matching Network (DIMN) for multi-choice reading comprehension. It consists of two major parts. Fusing Information from Passage and Question-option pair into Enhanced Embedding Representation (FEER) is proposed to draw associated information to enhance embedding representation, which incorporates the information that reflects the most salient supporting entities to answer the question into the contextualized representations; Linear Integration of Co-Attention and Convolution (LIAC) is proposed to capture the interactive information and local semantics to construct global interactive relationship, which incorporates local semantics of a single sequence into the question-option-aware passage and passage-aware question-option representation. The experiments are shown that our DIMN performs better accuracy on three datasets: RACE (69. 34%), DREAM (68. 45%) and MCTest (71. 81% on MCTest160 and 78. 83% on MCTest500). Our DIMN is beneficial for improving the ability of machines to understand natural language. The system we have developed has been applied to customer service support. Our source code is accessible at https: //github. com/vqiangv/DIMN.

Details DOI

NeurIPS Conference 2024 Conference Paper

Flaws can be Applause: Unleashing Potential of Segmenting Ambiguous Objects in SAM

Chenxin Li
Yuzhi Huang
Wuyang Li
Hengyu Liu
Xinyu Liu
Qing Xu
Zhen Chen
Yue Huang

As the vision foundation models like the Segment Anything Model (SAM) demonstrate potent universality, they also present challenges in giving ambiguous and uncertain predictions. Significant variations in the model output and granularity can occur with simply subtle changes in the prompt, contradicting the consensus requirement for the robustness of a model. While some established works have been dedicated to stabilizing and fortifying the prediction of SAM, this paper takes a unique path to explore how this flaw can be inverted into an advantage when modeling inherently ambiguous data distributions. We introduce an optimization framework based on a conditional variational autoencoder, which jointly models the prompt and the granularity of the object with a latent probability distribution. This approach enables the model to adaptively perceive and represent the real ambiguous label distribution, taming SAM to produce a series of diverse, convincing, and reasonable segmentation outputs controllably. Extensive experiments on several practical deployment scenarios involving ambiguity demonstrates the exceptional performance of our framework. Project page: \url{https: //a-sa-m. github. io/}.

PDF Details DOI

EAAI Journal 2024 Journal Article

G-HFIN: Graph-based Hierarchical Feature Integration Network for propaganda detection of We-media news articles

Xinyu Liu
Kun Ma
Qiang Wei
Ke Ji
Bo Yang
Ajith Abraham

In the era of We-media, articles are written by independently individuals that are not officially registered with the authorities. Propaganda hidden in the We-media articles have the potential to polarize public opinion and influence the mindset of the target audience. Currently, graph neural networks (GNNs) have been remarkably successful in Natural Language Processing (NLP). However, there are still some challenges to apply existing GNN-based for propaganda detection due to the limitation of extracting diverse word dependencies and capturing non-consecutive and long-range context. In this paper, we have proposed a Graph-based Hierarchical Feature Integration Network (abbreviated as G-HFIN) for Propaganda Detection. Specifically, semantic, syntactic, and sequential features are extracted to construct three heterogeneous graphs. Then, the Residual-connected Dual-layer Coarsening and Refining procedures (abbreviated as RDCR) are proposed to promote information interactions between distant nodes that are not directly connected, preserving both local and global node information during the intra-graph information propagation. Subsequently, an Attention-based Three-channel Feature Integration (abbreviated as ATFI) is proposed to harmonize sequence, semantic and syntactic information from three channels during the inter-graph information enhancement. Intra-graph and Inter-graph Joint Information Propagation is to implement homogeneous and heterogeneous information interaction respectively. Finally, these news representations are pooled and fed into the propaganda detection classifier. The experiments on three public datasets demonstrate that our model has outperformed state-of-the-art methods.

Details DOI

ICRA Conference 2023 Conference Paper

ADAPT: Action-aware Driving Caption Transformer

Bu Jin
Xinyu Liu
Yupeng Zheng
Pengfei Li 0007
Hao Zhao 0002
Tong Zhang
Yuhang Zheng 0004
Guyue Zhou

End-to-end autonomous driving has great potential in the transportation industry. However, the lack of transparency and interpretability of the automatic decision-making process hinders its industrial adoption in practice. There have been some early attempts to use attention maps or cost volume for better model explainability which is difficult for ordinary passengers to understand. To bridge the gap, we propose an end-to-end transformer-based architecture, ADAPT (Action-aware Driving cAPtion Transformer), which provides user-friendly natural language narrations and reasoning for each decision making step of autonomous vehicular control and action. ADAPT jointly trains both the driving caption task and the vehicular control prediction task, through a shared video representation. Experiments on BDD-X (Berkeley DeepDrive eXplanation) dataset demonstrate state-of-the-art performance of the ADAPT framework on both automatic metrics and human evaluation. To illustrate the feasibility of the proposed framework in real-world applications, we build a novel deployable system that takes raw car videos as input and outputs the action narrations and reasoning in real time. The code, models and data are available at https://github.com/jxbbb/ADAPT.

Details

IROS Conference 2023 Conference Paper

Robotic Barrier Construction through Weaved, Inflatable Tubes

Heather Jin Hee Kim
Haron Abdel-Raziq
Xinyu Liu
Alexandra Young Siskovic
Shreyas Dilip Patil
Kirstin Petersen
Hsin-Liu Cindy Kao

In this article, we present a mechanism and related path planning algorithm to construct light-duty barriers out of extruded, inflated tubes weaved around existing environmental features. Our extruded tubes are based on everted vine-robots and in this context, we present a new method to steer their growth. We characterize the mechanism in terms of accuracy resilience, and, towards their use as barriers, the ability of the tubes to withstand distributed loads. We further explore an algorithm which, given a feature map and the size and direction of the external load, can determine where and how to extrude the barrier. Finally, we showcase the potential of this method in an autonomously extruded two-layer wall weaved around three pipes. While preliminary, our work indicates that this method has potential for barrier construction in cluttered environments, e. g. shelters against wind or snow. Future work may show how to achieve tighter weaves, how to leverage weave friction for improved strength, how to assess barrier performance for feedback control, and how to operate the extrusion mechanism off of a mobile robot.

Details

AAAI Conference 2022 Conference Paper

SCAN: Cross Domain Object Detection with Semantic Conditioned Adaptation

Wuyang Li
Xinyu Liu
Xiwen Yao
Yixuan Yuan

The domain gap severely limits the transferability and scalability of object detectors trained in a specific domain when applied to a novel one. Most existing works bridge the domain gap by minimizing the domain discrepancy in the category space and aligning category-agnostic global features. Though great success, these methods model domain discrepancy with prototypes within a batch, yielding a biased estimation of domain-level distribution. Besides, the categoryagnostic alignment leads to the disagreement of class-specific distributions in the two domains, further causing inevitable classification errors. To overcome these two challenges, we propose a novel Semantic Conditioned AdaptatioN (SCAN) framework such that well-modeled unbiased semantics can support semantic conditioned adaptation for precise domain adaptive object detection. Specifically, class-specific semantics crossing different images in the source domain are graphically aggregated as the input to learn an unbiased semantic paradigm incrementally. The paradigm is then sent to a lightweight manifestation module to obtain conditional kernels to serve as the role of extracting semantics from the target domain for better adaptation. Subsequently, conditional kernels are integrated into global alignment to support the class-specific adaptation in a well-designed Conditional Kernel guided Alignment (CKA) module. Meanwhile, rich knowledge of the unbiased paradigm is transferred to the target domain with a novel Graph-based Semantic Transfer (GST) mechanism, yielding the adaptation in the categorybased feature space. Comprehensive experiments conducted on three adaptation benchmarks demonstrate that SCAN outperforms existing works by a large margin.

PDF Details

AAAI Conference 2019 Conference Paper

Generating Chinese Ci with Designated Metrical Structure

Richong Zhang
Xinyu Liu
Xinwei Chen
Zhiyuan Hu
Zhaoqing Xu
Yongyi Mao

Ci is a lyric poetry form that follows highly restrictive metrical structures. This makes it challenging for a computer to compose Ci subject to a specified metrical requirement. In this work, we adapt the CVAE framework to automated Ci generation under metrical constraints. Specifically, we present the first neural model that explicitly encodes the designated metrical structure for Ci generation. The proposed model is shown experimentally to generate Ci with nearly perfect metrical structures.

PDF Details

IS Journal 2019 Journal Article

Noncooperative Target Detection of Spacecraft Objects Based on Artificial Bee Colony Algorithm

Xinyu Liu
Donghui Li
Na Dong
Wai Hung Ip
Kai Leung Yung

Although heuristic algorithms have achieved the state-of-the-art performance for object detection, they have not been demonstrated to be sufficiently accurate and robust for multiobject detection. To address this problem, this article incorporates the concept of species into the artificial bee colony algorithm and proposes a multipeak optimization algorithm named species-based artificial bee colony (SABC). Then, we apply SABC to detect the noncooperative target (NCT) from two aspects: Multicircle detection and multitemplate matching. Experiments are conducted using real cases of “ShenZhou8” and “Apollo 9” space missions as well as the “Chang'e” camera point system developed by the Hong Kong Polytechnic University. Experimental results show that the proposed method is robust to detect NCT under various kinds of noise, weak light, and in-orbit and leads to accurate detection results with less time than other methods.

Details DOI

IROS Conference 2005 Conference Paper

A novel 3-DOF high acceleration/high precision robot optimal design and experimental verification

Qingyong Ding
Lining Sun
Xinyu Liu
Junhong Ji

This paper presents a novel 3-DOF hybrid kinematics robot composed of planar parallel mechanism including parallelogram linkages and linear drive, which are used for wire bonding of microelectronic device. Combining the advantages of parallel mechanism and direct drive, the robot can achieve high precision and high acceleration. The structure of the robot is optimized from the view of both kinematics and dynamics respectively. The experimental results demonstrated that the repeatability, acceleration and settling time of the robot can satisfy the proposed application.

Details