Jun Xiao

EAAI Journal 2026 Journal Article

Deep multi-modal fusion transformer for emotion recognition

• Qian Zhang
• Yifan Liu
• Biaokai Zhu
• Xun Han
• Ruilin Zhang
• Jun Xiao
• Zhe Wang

Multi-modal emotion recognition methods usually integrate peripheral and physiological information to extract complementary features. However, current multi-modal methods face shortcomings in spatio-temporal dependency modeling and feature complementary feature extraction across modalities, leading to limitations in accuracy and robustness for emotion recognition. To address these issues, this paper proposes a multi-modal emotion recognition network based on cross-modal transformer fusion to enhance the collaborative processing ability of electroencephalogram (EEG) and facial expression data. Specifically, we construct a fine and coarse combined transformer encoder for multi-level spatiotemporal feature extraction of EEG signals, and introduce a multi-modal cross-attention fusion transformer to achieve deep fusion of EEG, facial expression features and joint features, capturing dynamic relationships between and within modalities. Experimental results in two public datasets show that this method outperforms existing methods in accuracy and robustness, achieving a deep fusion of emotional dynamic and complex characteristics.

JBHI Journal 2026 Journal Article

Efficient Sleep Staging With Bayesian Uncertainty-Guided Active Learning

• Tianyou Yu
• Rui Huang
• Fei Wang
• Jun Zhang
• Wei Wu
• Zhuliang Yu
• Yuanqing Li
• Jun Xiao

Automated sleep staging is essential for large-scale and home-based sleep monitoring; however, in routine clinical practice, sleep annotation remains largely dependent on experienced experts performing time-consuming and labor-intensive manual scoring. Existing automatic systems often struggle to adapt reliably to new subjects, limiting their clinical adoption and reinforcing the reliance on expert review. This creates a strong demand for adaptive and efficient sleep staging systems that can substantially reduce annotation workload while preserving expert-level accuracy. We propose BayesSleepNet, a novel framework that integrates Bayesian uncertainty quantification with active learning for adaptive sleep staging. BayesSleepNet employs principled Bayesian modeling by placing distributions over network weights and performing Monte Carlo sampling at inference, enabling explicit quantification of model (epistemic) uncertainty. These uncertainty estimates drive a two-stage sample selection strategy that first fine-tunes the model using representative epochs and subsequently prioritizes persistently uncertain samples for expert review. Across four public sleep datasets, BayesSleepNet consistently improves performance—by 7. 60% in accuracy, 8. 27% in macro-F1, and 0. 104 in Cohen's $\kappa$ —while requiring manual annotation of only 20% of data from new subjects. Despite its adaptive learning capability, BayesSleepNet remains computationally lightweight, using substantially fewer parameters than representative high-capacity state-of-the-art models. These results demonstrate the clinical promise of uncertainty-aware active learning as a practical and cost-efficient paradigm for semi-automated sleep staging. Code is available at https://github.com/yuty2009/bayesugal.

AAAI Conference 2026 Conference Paper

GUI-G²: Gaussian Reward Modeling for GUI Grounding

• Fei Tang
• Zhangxuan Gu
• Zhengxi Lu
• Xuyang Liu
• Shuheng Shen
• Changhua Meng
• Wen Wang
• Wenqi Zhang

Graphical User Interface (GUI) grounding maps natural language instructions to precise interface locations for autonomous interaction. Current reinforcement learning approaches use binary rewards that treat elements as hit-or-miss targets, creating sparse signals that ignore the continuous nature of spatial interactions. Motivated by human clicking behavior that naturally forms Gaussian distributions centered on target elements, we introduce GUI Gaussian Grounding Rewards (GUI-G2), a principled reward framework that models GUI elements as continuous Gaussian distributions across the interface plane. GUI-G2 incorporates two synergistic mechanisms: Gaussian point rewards model precise localization through exponentially decaying distributions centered on element centroids, while coverage rewards assess spatial alignment by measuring the overlap between predicted Gaussian distributions and target regions. To handle diverse element scales, we develop an adaptive variance mechanism that calibrates reward distributions based on element dimensions. This framework transforms GUI grounding from sparse binary classification to dense continuous optimization, where Gaussian distributions generate rich gradient signals that guide models toward optimal interaction positions. Extensive experiments across ScreenSpot, ScreenSpot-v2, and ScreenSpot-Pro benchmarks demonstrate that GUI-G2, substantially outperforms state-of-the-art method UI-TARS-72B, with the most significant improvement of 24.7% on ScreenSpot-Pro. Our analysis reveals that continuous modeling provides superior robustness to interface variations and enhanced generalization to unseen layouts, establishing a new paradigm for spatial reasoning in GUI interaction tasks.

AAAI Conference 2026 Conference Paper

MAU-GPT: Enhancing Multi-type Industrial Anomaly Understanding via Anomaly-aware and Generalist Experts Adaptation

• Zhuonan Wang
• Zhenxuan Fan
• Siwen Tan
• Yu Zhong
• Yuqian Yuan
• Haoyuan Li
• Hao Jiang
• Wenqiao Zhang

As industrial manufacturing scales, automating fine-grained product image analysis has become critical for quality control. However, existing approaches are hindered by limited dataset coverage and poor model generalization across diverse and complex anomaly patterns. To address these challenges, we introduce MAU-Set, a comprehensive dataset for Multi-type industrial Anomaly Understanding. It spans multiple industrial domains and features a hierarchical task structure, ranging from binary classification to complex reasoning. Alongside this dataset, we establish a rigorous evaluation protocol to facilitate fair and comprehensive model assessment. Building upon this foundation, we further present MAU-GPT, a domain-adapted multimodal large model specifically designed for industrial anomaly understanding. It incorporates a novel AMoE-LoRA mechanism that unifies anomaly-aware and generalist experts adaptation, enhancing both understanding and reasoning across diverse defect classes. Extensive experiments show that MAU-GPT consistently outperforms prior state-of-the-art methods across all domains, demonstrating strong potential for scalable and automated industrial inspection.

AAAI Conference 2026 Conference Paper

TarPro: Targeted Protection Against Malicious Image Editing

• Kaixin Shen
• Ruijie Quan
• Jiaxu Miao
• Jun Xiao

The rapid advancement of diffusion-based image editing has enabled highly controllable visual content generation but has also raised serious concerns about the misuse of generative models for producing Not-Safe-for-Work (NSFW) content. Existing protection strategies inject adversarial perturbations to disrupt editing. However, these methods are untargeted, often degrading benign edits while failing to eliminate harmful outputs. In this work, we propose TarPro, a targeted protection framework that blocks malicious edits while preserving benign editing functionality. TarPro introduces Dual-Intent Optimization (DIO), a semantic alignment objective that suppresses malicious prompt effects while retaining desirable, benign edits, by leveraging prompt compositionality rather than requiring manually annotated preferences. To ensure robustness and generalization, we replace pixel-level optimization with a generator-based perturbation learning strategy that learns to produce structured, imperceptible perturbations in parameter space. Experiments on multiple diffusion backbones show that TarPro significantly blocks NSFW content while maintaining high-quality benign edits, outperforming strong baselines through both qualitative and quantitative evaluations.

NeurIPS Conference 2025 Conference Paper

Counterfactual Evolution of Multimodal Datasets via Visual Programming

• Minghe Gao
• Zhongqi Yue
• Wenjie Yan
• Yihao Hu
• Wei Ji
• Siliang Tang
• Jun Xiao
• Tat-Seng Chua

The rapid development of Multimodal Large Language Models (MLLMs) poses increasing demands on the diversity and complexity of multimodal datasets. Yet manual annotation pipelines can no longer keep pace. Existing augmentation methods often follow fixed rules and lack verifiable control over sample diversity and reasoning complexity. To address this, we introduce Scalable COunterfactual Program Evolution (SCOPE), a framework that uses symbolic Visual Programming to guide program evolution via counterfactual reasoning. SCOPE performs the three steps of counterfactual inference: (1) Abduction, by generating verifiable programs to model reasoning associations; (2) Action, by intervening on program structure along three axes—reasoning path, visual context, and cross-instance composition; and (3) Prediction, by categorizing evolved instances by difficulty, structure, and input multiplicity. Based on this process, we build SCOPE-Train and SCOPE-Test, evolving benchmarks with expert validation. To support training, we propose MAP, a curriculum learning strategy that aligns model capacity with sample difficulty. Experiments show that SCOPE improves reasoning performance, exposes model blind spots, and enhances visual dialog capabilities.

NeurIPS Conference 2025 Conference Paper

EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?

• Yuqian Yuan
• Ronghao Dang
• Long Li
• Wentong Li
• Dian Jiao
• Xin Li
• Deli Zhao
• Fan Wang

The emergence of multimodal large language models (MLLMs) has driven breakthroughs in egocentric vision applications. These applications necessitate persistent, context-aware understanding of objects, as users interact with tools in dynamic and cluttered environments. However, existing embodied benchmarks primarily focus on static scene exploration, emphasizing object's appearance and spatial attributes while neglecting the assessment of dynamic changes arising from users' interactions. capabilities in object-level spatiotemporal reasoning required for real-world interactions. To address this gap, we introduce EOC-Bench, an innovative benchmark designed to systematically evaluate object-centric embodied cognition in dynamic egocentric scenarios. Specially, EOC-Bench features 3, 277 meticulously annotated QA pairs categorized into three temporal categories: Past, Present, and Future, covering 11 fine-grained evaluation dimensions and 3 visual object referencing types. To ensure thorough assessment, we develop a mixed-format human-in-the-loop annotation frameworkBased on EOC-Bench, we conduct comprehensive evaluations of various proprietary, open-source, and object-level MLLMs. EOC-Bench serves as a crucial tool for advancing the embodied object cognitive capabilities of MLLMs, establishing a robust foundation for developing reliable core models for embodied systems.

NeurIPS Conference 2025 Conference Paper

Janus-Pro-R1: Advancing Collaborative Visual Comprehension and Generation via Reinforcement Learning

• Kaihang Pan
• Yang Wu
• Wendong Bu
• Shen Kai
• Juncheng Li
• Yingting Wang
• Yunfei Li
• Siliang Tang

Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify visual comprehension and generation. However, these two capabilities remain largely independent, as if they are two separate functions encapsulated within the same model. Consequently, visual comprehension does not enhance visual generation, and the reasoning mechanisms of LLMs have not been fully integrated to revolutionize image generation. In this paper, we propose to enable the collaborative co-evolution of visual comprehension and generation, advancing image generation into an iterative introspective process. We introduce a two-stage training approach: supervised fine-tuning teaches the MLLM with the foundational ability to generate genuine CoT for visual generation, while reinforcement learning activates its full potential via an exploration-exploitation trade-off. Ultimately, we unlock the Aha moment in visual generation, advancing MLLMs from text-to-image tasks to unified image generation. Extensive experiments demonstrate that our model not only excels in text-to-image generation and image editing, but also functions as a superior image semantic evaluator with enhanced visual comprehension capabilities. Project Page: \url{https: //janus-pro-r1. github. io}.

NeurIPS Conference 2025 Conference Paper

Let LRMs Break Free from Overthinking via Self-Braking Tuning

• Haoran Zhao
• Yuchen Yan
• Yongliang Shen
• Haolei Xu
• Wenqi Zhang
• Kaitao Song
• Jian Shao
• Weiming Lu

Large reasoning models (LRMs), such as OpenAI o1 and DeepSeek-R1, have significantly enhanced their reasoning capabilities by generating longer chains of thought, demonstrating outstanding performance across a variety of tasks. However, this performance gain comes at the cost of a substantial increase in redundant reasoning during the generation process, leading to high computational overhead and exacerbating the issue of overthinking. Although numerous existing approaches aim to address the problem of overthinking, they often rely on external interventions. In this paper, we propose a novel framework, Self-Braking Tuning (SBT), which tackles overthinking from the perspective of allowing the model to regulate its own reasoning process, thus eliminating the reliance on external control mechanisms. We construct a set of overthinking identification metrics based on standard answers and design a systematic method to detect redundant reasoning. This method accurately identifies unnecessary steps within the reasoning trajectory and generates training signals for learning self-regulation behaviors. Building on this foundation, we develop a complete strategy for constructing data with adaptive reasoning lengths and introduce an innovative braking prompt mechanism that enables the model to naturally learn when to terminate reasoning at an appropriate point. Experiments across mathematical benchmarks (AIME, AMC, MATH500, GSM8K) demonstrate that our method reduces token consumption by up to 60\% while maintaining comparable accuracy to unconstrained models.

ECAI Conference 2025 Conference Paper

MCLPD: Multi-View Contrastive Learning for EEG-Based PD Detection Across Datasets

• Qian Zhang
• Ruilin Zhang
• Jun Xiao
• Yifan Liu
• Zhe Wang

Electroencephalography has been validated as an effective technique for detecting Parkinson’s disease, particularly in its early stages. However, the high cost of EEG data annotation often results in limited dataset size and considerable discrepancies across datasets, including differences in acquisition protocols and subject demographics, significantly hinder the robustness and generalizability of models in cross-dataset detection scenarios. To address such challenges, this paper proposes a semi-supervised learning framework named MCLPD, which integrates multi-view contrastive pre-training with lightweight supervised fine-tuning to enhance cross-dataset PD detection performance. During pre-training, MCLPD uses self-supervised learning on the unlabeled UNM dataset. To build contrastive pairs, it applies dual augmentations in both time and frequency domains, which enrich the data and naturally fuse time-frequency information. In the fine-tuning phase, only a small proportion of labeled data from another two datasets (UI and UC) is used for supervised optimization. Experimental results show that MCLPD achieves F1 scores of 0. 91 on UI and 0. 81 on UC using only 1% of labeled data, which further improve to 0. 97 and 0. 87, respectively, when 5% of labeled data is used. Compared to existing methods, MCLPD substantially improves cross-dataset generalization while reducing the dependency on labeled data, demonstrating the effectiveness of the proposed framework.

NeurIPS Conference 2025 Conference Paper

Mind the Gap: Bridging Thought Leap for Improved Chain-of-Thought Tuning

• Haolei Xu
• Yuchen Yan
• Yongliang Shen
• Wenqi Zhang
• Guiyang Hou
• Shengpei Jiang
• Kaitao Song
• Weiming Lu

Large language models (LLMs) have achieved remarkable progress on mathematical tasks through Chain-of-Thought (CoT) reasoning. However, existing mathematical CoT datasets often suffer from Thought Leaps due to experts omitting intermediate steps, which negatively impacts model learning and generalization. We propose the CoT Thought Leap Bridge Task, which aims to automatically detect leaps and generate missing intermediate reasoning steps to restore the completeness and coherence of CoT. To facilitate this, we constructed a specialized training dataset called ScaleQM+, based on the structured ScaleQuestMath dataset, and trained CoT-Bridge to bridge thought leaps. Through comprehensive experiments on mathematical reasoning benchmarks, we demonstrate that models fine-tuned on bridged datasets consistently outperform those trained on original datasets, with improvements of up to +5. 87\% on NuminaMath. Our approach effectively enhances distilled data (+3. 02\%) and provides better starting points for reinforcement learning (+3. 1\%), functioning as a plug-and-play module compatible with existing optimization techniques. Furthermore, CoT-Bridge demonstrates improved generalization to out-of-domain logical reasoning tasks, confirming that enhancing reasoning completeness yields broadly applicable benefits.

JBHI Journal 2025 Journal Article

Multi-Scale Spatio-Temporal Attention Network for Epileptic Seizure Prediction

• Qiulei Dong
• Han Zhang
• Jun Xiao
• Jiayin Sun

Epilepticseizure prediction from electroencephalogram (EEG) data has attracted much attention in the clinical diagnosis and treatment of epilepsy. Most of the existing methods in literature extract either spatial or temporal features at a single scale from EEG data, however, their learned features are generally less discriminative since the EEG data is complex and severely noisy in general, leading to low-accuracy predictions. To address this problem, we propose a Multi-scale Spatio-temporal Attention Network to learn discriminative features for seizure prediction, called MSAN, which contains a backbone module, a spatial pyramid module, and a multi-scale sequential aggregation module. The backbone module is to extract initial spatial features from the input EEG spectrograms, and the pyramid module is introduced to learn multi-scale features from the initial features. Then by taking these multi-scale features as input temporal features, the sequential aggregation module employs multiple Long Short-Term Memory(LSTM) blocks to aggregate these features. In addition, a dual-loss function is introduced to alleviate the class imbalance problem. The proposed method achieves an average sensitivity of 96. 27% with a mean false prediction rate of 0. 00/h on the CHB-MIT dataset and an average sensitivity of 93. 57% with a mean false prediction rate of 0. 044/h on the Kaggle dataset. The comparative results demonstrate that the proposed method outperforms 10 state-of-the-art epileptic seizure prediction models.

NeurIPS Conference 2025 Conference Paper

SegGraph: Leveraging Graphs of SAM Segments for Few-Shot 3D Part Segmentation

• Yueyang Hu
• Haiyong Jiang
• Haoxuan Song
• Jun Xiao
• Hao Pan

This work presents a novel framework for few-shot 3D part segmentation. Recent advances have demonstrated the significant potential of 2D foundation models for low-shot 3D part segmentation. However, it is still an open problem that how to effectively aggregate 2D knowledge from foundation models to 3D. Existing methods either ignore geometric structures for 3D feature learning or neglects the high-quality grouping clues from SAM, leading to under-segmentation and inconsistent part labels. We devise a novel SAM segment graph-based propagation method, named SegGraph, to explicitly learn geometric features encoded within SAM's segmentation masks. Our method encodes geometric features by modeling mutual overlap and adjacency between segments while preserving intra-segment semantic consistency. We construct a segment graph, conceptually similar to an atlas, where nodes represent segments and edges capture their spatial relationships (overlap/adjacency). Each node adaptively modulates 2D foundation model features, which are then propagated via a graph neural network to learn global geometric structures. To enforce intra-segment semantic consistency, we map segment features to 3D points with a novel view-direction-weighted fusion attenuating contributions from low-quality segments. Extensive experiments on PartNet-E demonstrate that our method outperforms all competing baselines by at least 6. 9% mIoU. Further analysis reveals that SegGraph achieves particularly strong performance on small components and part boundaries, demonstrating its superior geometric understanding.

ICML Conference 2025 Conference Paper

The Four Color Theorem for Cell Instance Segmentation

• Ye Zhang 0008
• Yu Zhou
• Yifeng Wang 0001
• Jun Xiao
• Ziyue Wang 0005
• Yongbing Zhang 0002
• Jianxu Chen 0001

Cell instance segmentation is critical to analyzing biomedical images, yet accurately distinguishing tightly touching cells remains a persistent challenge. Existing instance segmentation frameworks, including detection-based, contour-based, and distance mapping-based approaches, have made significant progress, but balancing model performance with computational efficiency remains an open problem. In this paper, we propose a novel cell instance segmentation method inspired by the four-color theorem. By conceptualizing cells as countries and tissues as oceans, we introduce a four-color encoding scheme that ensures adjacent instances receive distinct labels. This reformulation transforms instance segmentation into a constrained semantic segmentation problem with only four predicted classes, substantially simplifying the instance differentiation process. To solve the training instability caused by the non-uniqueness of four-color encoding, we design an asymptotic training strategy and encoding transformation method. Extensive experiments on various modes demonstrate our approach achieves state-of-the-art performance. The code is available at https: //github. com/zhangye-zoe/FCIS.

NeurIPS Conference 2024 Conference Paper

$\text{Di}^2\text{Pose}$: Discrete Diffusion Model for Occluded 3D Human Pose Estimation

• Weiquan Wang
• Jun Xiao
• Chunping Wang
• Wei Liu
• Zhao Wang
• Long Chen

Diffusion models have demonstrated their effectiveness in addressing the inherent uncertainty and indeterminacy in monocular 3D human pose estimation (HPE). Despite their strengths, the need for large search spaces and the corresponding demand for substantial training data make these models prone to generating biomechanically unrealistic poses. This challenge is particularly noticeable in occlusion scenarios, where the complexity of inferring 3D structures from 2D images intensifies. In response to these limitations, we introduce the **Di**screte **Di**ffusion **Pose** (**$\text{Di}^2\text{Pose}$**), a novel framework designed for occluded 3D HPE that capitalizes on the benefits of a discrete diffusion model. Specifically, **$\text{Di}^2\text{Pose}$** employs a two-stage process: it first converts 3D poses into a discrete representation through a pose quantization step, which is subsequently modeled in latent space through a discrete diffusion process. This methodological innovation restrictively confines the search space towards physically viable configurations and enhances the model’s capability to comprehend how occlusions affect human pose within the latent space. Extensive evaluations conducted on various benchmarks (e. g. , Human3. 6M, 3DPW, and 3DPW-Occ) have demonstrated its effectiveness.

AAAI Conference 2024 Conference Paper

CoreRec: A Counterfactual Correlation Inference for Next Set Recommendation

• Kexin Li
• Chengjiang Long
• Shengyu Zhang
• Xudong Tang
• Zhichao Zhai
• Kun Kuang
• Jun Xiao

Next set recommendation aims to predict the items that are likely to be bought in the next purchase. Central to this endeavor is the task of capturing intra-set and cross-set correlations among items. However, the modeling of cross-set correlations poses challenges due to specific issues. Primarily, these correlations are often implicit, and the prevailing approach of establishing an indiscriminate link across the entire set of objects neglects factors like purchase frequency and correlations between purchased items. Such hastily formed connections across sets introduce substantial noise. Additionally, the preeminence of high-frequency items in numerous sets could potentially overshadow and distort correlation modeling with respect to low-frequency items. Thus, we devoted to mitigating misleading inter-set correlations. With a fresh perspective rooted in causality, we delve into the question of whether correlations between a particular item and items from other sets should be relied upon for item representation learning and set prediction. Technically, we introduce the Counterfactual Correlation Inference framework for next set recommendation, denoted as CoreRec. This framework establishes a counterfactual scenario in which the recommendation model impedes cross-set correlations to generate intervened predictions. By contrasting these intervened predictions with the original ones, we gauge the causal impact of inter-set neighbors on set prediction—essentially assessing whether they contribute to spurious correlations. During testing, we introduce a post-trained switch module that selects between set-aware item representations derived from either the original or the counterfactual scenarios. To validate our approach, we extensively experiment using three real-world datasets, affirming both the effectiveness of CoreRec and the cogency of our analytical approach.

AAAI Conference 2024 Conference Paper

Existence Is Chaos: Enhancing 3D Human Motion Prediction with Uncertainty Consideration

• Zhihao Wang
• Yulin Zhou
• Ningyu Zhang
• Xiaosong Yang
• Jun Xiao
• Zhao Wang

Human motion prediction is consisting in forecasting future body poses from historically observed sequences. It is a longstanding challenge due to motion's complex dynamics and uncertainty. Existing methods focus on building up complicated neural networks to model the motion dynamics. The predicted results are required to be strictly similar to the training samples with L2 loss in current training pipeline. However, little attention has been paid to the uncertainty property which is crucial to the prediction task. We argue that the recorded motion in training data could be an observation of possible future, rather than a predetermined result. In addition, existing works calculate the predicted error on each future frame equally during training, while recent work indicated that different frames could play different roles. In this work, a novel computationally efficient encoder-decoder model with uncertainty consideration is proposed, which could learn proper characteristics for future frames by a dynamic function. Experimental results on benchmark datasets demonstrate that our uncertainty consideration approach has obvious advantages both in quantity and quality. Moreover, the proposed method could produce motion sequences with much better quality that avoids the intractable shaking artefacts. We believe our work could provide a novel perspective to consider the uncertainty quality for the general motion prediction task and encourage the studies in this field. The code will be available in https://github.com/Motionpre/Adaptive-Salient-Loss-SAGGB.

AAAI Conference 2024 Conference Paper

Fully Data-Driven Pseudo Label Estimation for Pointly-Supervised Panoptic Segmentation

• Jing Li
• Junsong Fan
• Yuran Yang
• Shuqi Mei
• Jun Xiao
• Zhaoxiang Zhang

The core of pointly-supervised panoptic segmentation is estimating accurate dense pseudo labels from sparse point labels to train the panoptic head. Previous works generate pseudo labels mainly based on hand-crafted rules, such as connecting multiple points into polygon masks, or assigning the label information of labeled pixels to unlabeled pixels based on the artificially defined traversing distance. The accuracy of pseudo labels is limited by the quality of the hand-crafted rules (polygon masks are rough at object contour regions, and the traversing distance error will result in wrong pseudo labels). To overcome the limitation of hand-crafted rules, we estimate pseudo labels with a fully data-driven pseudo label branch, which is optimized by point labels end-to-end and predicts more accurate pseudo labels than previous methods. We also train an auxiliary semantic branch with point labels, it assists the training of the pseudo label branch by transferring semantic segmentation knowledge through shared parameters. Experiments on Pascal VOC and MS COCO demonstrate that our approach is effective and shows state-of-the-art performance compared with related works. Codes are available at https://github.com/BraveGroup/FDD.

NeurIPS Conference 2023 Conference Paper

Decompose Novel into Known: Part Concept Learning For 3D Novel Class Discovery

• Tingyu Weng
• Jun Xiao
• Haiyong Jiang

In this work, we address 3D novel class discovery (NCD) that discovers novel classes from an unlabeled dataset by leveraging the knowledge of disjoint known classes. The key challenge of 3D NCD is that learned features by known class recognition are heavily biased and hinder generalization to novel classes. Since geometric parts are more generalizable across different classes, we propose to decompose novel into known parts, coined DNIK, to mitigate the above problems. DNIK learns a part concept bank encoding rich part geometric patterns from known classes so that novel 3D shapes can be represented as part concept compositions to facilitate cross-category generalization. Moreover, we formulate three constraints on part concepts to ensure diverse part concepts without collapsing. A part relation encoding module (PRE) is also developed to leverage part-wise spatial relations for better recognition. We construct three 3D NCD tasks for evaluation and extensive experiments show that our method achieves significantly superior results than SOTA baselines (+11. 7%, +14. 1%, and +16. 3% improvements on average for three tasks, respectively). Code and data will be released.

NeurIPS Conference 2023 Conference Paper

Two Heads are Better Than One: A Simple Exploration Framework for Efficient Multi-Agent Reinforcement Learning

• Jiahui Li
• Kun Kuang
• Baoxiang Wang
• Xingchen Li
• Fei Wu
• Jun Xiao
• Long Chen

Exploration strategy plays an important role in reinforcement learning, especially in sparse-reward tasks. In cooperative multi-agent reinforcement learning~(MARL), designing a suitable exploration strategy is much more challenging due to the large state space and the complex interaction among agents. Currently, mainstream exploration methods in MARL either contribute to exploring the unfamiliar states which are large and sparse, or measuring the interaction among agents with high computational costs. We found an interesting phenomenon that different kinds of exploration plays a different role in different MARL scenarios, and choosing a suitable one is often more effective than designing an exquisite algorithm. In this paper, we propose a exploration method that incorporate the \underline{C}uri\underline{O}sity-based and \underline{IN}fluence-based exploration~(COIN) which is simple but effective in various situations. First, COIN measures the influence of each agent on the other agents based on mutual information theory and designs it as intrinsic rewards which are applied to each individual value function. Moreover, COIN computes the curiosity-based intrinsic rewards via prediction errors which are added to the extrinsic reward. For integrating the two kinds of intrinsic rewards, COIN utilizes a novel framework in which they complement each other and lead to a sufficient and effective exploration on cooperative MARL tasks. We perform extensive experiments on different challenging benchmarks, and results across different scenarios show the superiority of our method.

NeurIPS Conference 2023 Conference Paper

Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models

• Lin Li
• Jun Xiao
• Guikun Chen
• Jian Shao
• Yueting Zhuang
• Long Chen

Pretrained vision-language models, such as CLIP, have demonstrated strong generalization capabilities, making them promising tools in the realm of zero-shot visual recognition. Visual relation detection (VRD) is a typical task that identifies relationship (or interaction) types between object pairs within an image. However, naively utilizing CLIP with prevalent class-based prompts for zero-shot VRD has several weaknesses, e. g. , it struggles to distinguish between different fine-grained relation types and it neglects essential spatial information of two objects. To this end, we propose a novel method for zero-shot VRD: RECODE, which solves RElation detection via COmposite DEscription prompts. Specifically, RECODE first decomposes each predicate category into subject, object, and spatial components. Then, it leverages large language models (LLMs) to generate description-based prompts (or visual cues) for each component. Different visual cues enhance the discriminability of similar relation categories from different perspectives, which significantly boosts performance in VRD. To dynamically fuse different cues, we further introduce a chain-of-thought method that prompts LLMs to generate reasonable weights for different visual cues. Extensive experiments on four VRD benchmarks have demonstrated the effectiveness and interpretability of RECODE.

AAAI Conference 2022 Conference Paper

ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation

• Chuanqing Zhuang
• Zhengda Lu
• Yiqun Wang
• Jun Xiao
• Ying Wang

Depth estimation is a crucial step for 3D reconstruction with panorama images in recent years. Panorama images maintain the complete spatial information but introduce distortion with equirectangular projection. In this paper, we propose an ACDNet based on the adaptively combined dilated convolution to predict the dense depth map for a monocular panoramic image. Specifically, we combine the convolution kernels with different dilations to extend the receptive field in the equirectangular projection. Meanwhile, we introduce an adaptive channel-wise fusion module to summarize the feature maps and get diverse attention areas in the receptive field along the channels. Due to the utilization of channel-wise attention in constructing the adaptive channel-wise fusion module, the network can capture and leverage the cross-channel contextual information efficiently. Finally, we conduct depth estimation experiments on three datasets (both virtual and real-world) and the experimental results demonstrate that our proposed ACDNet substantially outperforms the current state-of-the-art (SOTA) methods. Our codes and model parameters are accessed in https: //github. com/zcq15/ACDNet.

NeurIPS Conference 2022 Conference Paper

SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization

• Chuanyang Zheng
• Zheyang Li
• Kai Zhang
• Zhi Yang
• Wenming Tan
• Jun Xiao
• Ye Ren
• Shiliang Pu

Vision Transformers (ViTs) yield impressive performance across various vision tasks. However, heavy computation and memory footprint make them inaccessible for edge devices. Previous works apply importance criteria determined independently by each individual component to prune ViTs. Considering that heterogeneous components in ViTs play distinct roles, these approaches lead to suboptimal performance. In this paper, we introduce joint importance, which integrates essential structural-aware interactions between components for the first time, to perform collaborative pruning. Based on the theoretical analysis, we construct a Taylor-based approximation to evaluate the joint importance. This guides pruning toward a more balanced reduction across all components. To further reduce the algorithm complexity, we incorporate the interactions into the optimization function under some mild assumptions. Moreover, the proposed method can be seamlessly applied to various tasks including object detection. Extensive experiments demonstrate the effectiveness of our method. Notably, the proposed approach outperforms the existing state-of-the-art approaches on ImageNet, increasing accuracy by 0. 7% over the DeiT-Base baseline while saving 50% FLOPs. On COCO, we are the first to show that 70% FLOPs of FasterRCNN with ViT backbone can be removed with only 0. 3% mAP drop. The code is available at https: //github. com/hikvision-research/SAViT.

AAAI Conference 2021 Conference Paper

Boundary Proposal Network for Two-stage Natural Language Video Localization

• Shaoning Xiao
• Long Chen
• Songyang Zhang
• Wei Ji
• Jian Shao
• Lu Ye
• Jun Xiao

We aim to address the problem of Natural Language Video Localization (NLVL) — localizing the video segment corresponding to a natural language description in a long and untrimmed video. State-of-the-art NLVL methods are almost in one-stage fashion, which can be typically grouped into two categories: 1) anchor-based approach: it first pre-defines a series of video segment candidates (e. g. , by sliding window), and then does classification for each candidate; 2) anchor-free approach: it directly predicts the probabilities for each video frame1 as a boundary or intermediate frame inside the positive segment. However, both kinds of one-stage approaches have inherent drawbacks: the anchor-based approach is susceptible to the heuristic rules, further limiting the capability of handling videos with variant length. While the anchorfree approach fails to exploit the segment-level interaction thus achieving inferior results. In this paper, we propose a novel Boundary Proposal Network (BPNet), a universal twostage framework that gets rid of the issues mentioned above. Specifically, in the first stage, BPNet utilizes an anchor-free model to generate a group of high-quality candidate video segments with their boundaries. In the second stage, a visuallanguage fusion layer is proposed to jointly model the multimodal interaction between the candidate and the language query, followed by a matching score rating layer that outputs the alignment score for each candidate. We evaluate our BP- Net on three challenging NLVL benchmarks (i. e. , Charades- STA, TACoS and ActivityNet-Captions). Extensive experiments and ablative studies on these datasets demonstrate that the BPNet outperforms the state-of-the-art methods.

AAAI Conference 2021 Conference Paper

Consensus Graph Representation Learning for Better Grounded Image Captioning

• Wenqiao Zhang
• Haochen Shi
• Siliang Tang
• Jun Xiao
• Qiang Yu
• Yueting Zhuang

The contemporary visual captioning models frequently hallucinate objects that are not actually in a scene, due to the visual misclassification or over-reliance on priors that resulting in the semantic inconsistency between the visual information and the target lexical words. The most common way is to encourage the captioning model to dynamically link generated object words or phrases to appropriate regions of the image, i. e. , the grounded image captioning (GIC). However, GIC utilizes an auxiliary task (grounding objects) that has not solved the key issue of object hallucination, i. e. , the semantic inconsistency. In this paper, we take a novel perspective on the issue above: exploiting the semantic coherency between the visual and language modalities. Specifically, we propose the Consensus Rraph Representation Learning framework (CGRL) for GIC that incorporates a consensus representation into the grounded captioning pipeline. The consensus is learned by aligning the visual graph (e. g. , scene graph) to the language graph that consider both the nodes and edges in a graph. With the aligned consensus, the captioning model can capture both the correct linguistic characteristics and visual relevance, and then grounding appropriate image regions further. We validate the effectiveness of our model, with a significant decline in object hallucination (-9% CHAIRi) on the Flickr30k Entities dataset. Besides, our CGRL also evaluated by several automatic metrics and human evaluation, the results indicate that the proposed approach can simultaneously improve the performance of image captioning (+2. 9 Cider) and grounding (+2. 3 F1LOC ).

AAAI Conference 2021 Conference Paper

Ref-NMS: Breaking Proposal Bottlenecks in Two-Stage Referring Expression Grounding

• Long Chen
• Wenbo Ma
• Jun Xiao
• Hanwang Zhang
• Shih-Fu Chang

The prevailing framework for solving referring expression grounding is based on a two-stage process: 1) detecting proposals with an object detector and 2) grounding the referent to one of the proposals. Existing two-stage solutions mostly focus on the grounding step, which aims to align the expressions with the proposals. In this paper, we argue that these methods overlook an obvious mismatch between the roles of proposals in the two stages: they generate proposals solely based on the detection confidence (i. e. , expression-agnostic), hoping that the proposals contain all right instances in the expression (i. e. , expression-aware). Due to this mismatch, current two-stage methods suffer from a severe performance drop between detected and ground-truth proposals. To this end, we propose Ref-NMS, which is the first method to yield expression-aware proposals at the first stage. Ref-NMS regards all nouns in the expression as critical objects, and introduces a lightweight module to predict a score for aligning each box with a critical object. These scores can guide the NMS operation to filter out the boxes irrelevant to the expression, increasing the recall of critical objects, resulting in a significantly improved grounding performance. Since Ref- NMS is agnostic to the grounding step, it can be easily integrated into any state-of-the-art two-stage method. Extensive ablation studies on several backbones, benchmarks, and tasks consistently demonstrate the superiority of Ref-NMS. Codes are available at: https: //github. com/ChopinSharp/ref-nms.

AAAI Conference 2020 Conference Paper

CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation

• Junsong Fan
• Zhaoxiang Zhang
• Tieniu Tan
• Chunfeng Song
• Jun Xiao

Weakly supervised semantic segmentation with only imagelevel labels saves large human effort to annotate pixel-level labels. Cutting-edge approaches rely on various innovative constraints and heuristic rules to generate the masks for every single image. Although great progress has been achieved by these methods, they treat each image independently and do not take account of the relationships across different images. In this paper, however, we argue that the cross-image relationship is vital for weakly supervised segmentation. Because it connects related regions across images, where supplementary representations can be propagated to obtain more consistent and integral regions. To leverage this information, we propose an end-to-end cross-image affinity module, which exploits pixel-level cross-image relationships with only imagelevel labels. By means of this, our approach achieves 64. 3% and 65. 3% mIoU on Pascal VOC 2012 validation and test set respectively, which is a new state-of-the-art result by only using image-level labels for weakly supervised semantic segmentation, demonstrating the superiority of our approach.

IJCAI Conference 2020 Conference Paper

Hierarchical Attention Based Spatial-Temporal Graph-to-Sequence Learning for Grounded Video Description

• Kai Shen
• Lingfei Wu
• Fangli Xu
• Siliang Tang
• Jun Xiao
• Yueting Zhuang

The task of Grounded Video Description~(GVD) is to generate sentences whose objects can be grounded with the bounding boxes in the video frames. Existing works often fail to exploit structural information both in modeling the relationships among the region proposals and in attending them for text generation. To address these issues, we cast the GVD task as a spatial-temporal Graph-to-Sequence learning problem, where we model video frames as spatial-temporal sequence graph in order to better capture implicit structural relationships. In particular, we exploit two ways to construct a sequence graph that captures spatial-temporal correlations among different objects in each frame and further present a novel graph topology refinement technique to discover optimal underlying graph structure. In addition, we also present hierarchical attention mechanism to attend sequence graph in different resolution levels for better generating the sentences. Our extensive experiments demonstrate the effectiveness of our proposed method compared to state-of-the-art methods.

AAAI Conference 2020 Conference Paper

Rethinking the Bottom-Up Framework for Query-Based Video Localization

• Long Chen
• Chujie Lu
• Siliang Tang
• Jun Xiao
• Dong Zhang
• Chilie Tan
• Xiaolin Li

In this paper, we focus on the task query-based video localization, i. e. , localizing a query in a long and untrimmed video. The prevailing solutions for this problem can be grouped into two categories: i) Top-down approach: It pre-cuts the video into a set of moment candidates, then it does classification and regression for each candidate; ii) Bottom-up approach: It injects the whole query content into each video frame, then it predicts the probabilities of each frame as a ground truth segment boundary (i. e. , start or end). Both two frameworks have respective shortcomings: the top-down models suffer from heavy computations and they are sensitive to the heuristic rules, while the performance of bottom-up models is behind the performance of top-down counterpart thus far. However, we argue that the performance of bottom-up framework is severely underestimated by current unreasonable designs, including both the backbone and head network. To this end, we design a novel bottom-up model: Graph-FPN with Dense Predictions (GDP). For the backbone, GDP firstly generates a frame feature pyramid to capture multi-level semantics, then it utilizes graph convolution to encode the plentiful scene relationships, which incidentally mitigates the semantic gaps in the multi-scale feature pyramid. For the head network, GDP regards all frames falling in the ground truth segment as the foreground, and each foreground frame regresses the unique distances from its location to bi-directional boundaries. Extensive experiments on two challenging query-based video localization tasks (natural language video localization and video relocalization), involving four challenging benchmarks (TACoS, Charades-STA, ActivityNet Captions, and Activity- VRL), have shown that GDP surpasses the state-of-the-art top-down models.

IJCAI Conference 2019 Conference Paper

Weak Supervision Enhanced Generative Network for Question Generation

• Yutong Wang
• Jiyuan Zheng
• Qijiong Liu
• Zhou Zhao
• Jun Xiao
• Yueting Zhuang

Automatic question generation according to an answer within the given passage is useful for many applications, such as question answering system, dialogue system, etc. Current neural-based methods mostly take two steps which extract several important sentences based on the candidate answer through manual rules or supervised neural networks and then use an encoder-decoder framework to generate questions about these sentences. These approaches still acquire two steps and neglect the semantic relations between the answer and the context of the whole passage which is sometimes necessary for answering the question. To address this problem, we propose the Weakly Supervision Enhanced Generative Network (WeGen) which automatically discovers relevant features of the passage given the answer span in a weakly supervised manner to improve the quality of generated questions. More specifically, we devise a discriminator, Relation Guider, to capture the relations between the passage and the associated answer and then the Multi-Interaction mechanism is deployed to transfer the knowledge dynamically for our question generation system. Experiments show the effectiveness of our method in both automatic evaluations and human evaluations.

IJCAI Conference 2018 Conference Paper

Attentional Image Retweet Modeling via Multi-Faceted Ranking Network Learning

• Zhou Zhao
• Lingtao Meng
• Jun Xiao
• Min Yang
• Fei Wu
• Deng Cai
• Xiaofei He
• Yueting Zhuang

Retweet prediction is a challenging problem in social media sites (SMS). In this paper, we study the problem of image retweet prediction in social media, which predicts the image sharing behavior that the user reposts the image tweets from their followees. Unlike previous studies, we learn user preference ranking model from their past retweeted image tweets in SMS. We first propose heterogeneous image retweet modeling network (IRM) that exploits users' past retweeted image tweets with associated contexts, their following relations in SMS and preference of their followees. We then develop a novel attentional multi-faceted ranking network learning framework with multi-modal neural networks for the proposed heterogenous IRM network to learn the joint image tweet representations and user preference representations for prediction task. The extensive experiments on a large-scale dataset from Twitter site shows that our method achieves better performance than other state-of-the-art solutions to the problem.

IJCAI Conference 2018 Conference Paper

Multi-Turn Video Question Answering via Multi-Stream Hierarchical Attention Context Network

• Zhou Zhao
• Xinghua Jiang
• Deng Cai
• Jun Xiao
• Xiaofei He
• Shiliang Pu

Conversational video question answering is a challenging task in visual information retrieval, which generates the accurate answer from the referenced video contents according to the visual conversation context and given question. However, the existing visual question answering methods mainly tackle the problem of single-turn video question answering, which may be ineffectively applied for multi-turn video question answering directly, due to the insufficiency of modeling the sequential conversation context. In this paper, we study the problem of multi-turn video question answering from the viewpoint of multi-step hierarchical attention context network learning. We first propose the hierarchical attention context network for context-aware question understanding by modeling the hierarchically sequential conversation context structure. We then develop the multi-stream spatio-temporal attention network for learning the joint representation of the dynamic video contents and context-aware question embedding. We next devise the hierarchical attention context network learning method with multi-step reasoning process for multi-turn video question answering. We construct two large-scale multi-turn video question answering datasets. The extensive experiments show the effectiveness of our method.

IJCAI Conference 2017 Conference Paper

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

• Jun Xiao
• Hao Ye
• Xiangnan He
• Hanwang Zhang
• Fei Wu
• Tat-Seng Chua

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8. 6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al. , 2016] and DeepCross [Shan et al. , 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https: //github. com/hexiangnan/attentional_factorization_machine

IJCAI Conference 2016 Conference Paper

Diverse Image Captioning via GroupTalk

• Zhuhao Wang
• Fei Wu
• Weiming Lu
• Jun Xiao
• Xi Li
• Zitong Zhang
• Yueting Zhuang

Generally speaking, different persons tend to describe images from various aspects due to their individually subjective perception. As a result, generating the appropriate descriptions of images with both diversity and high quality is of great importance. In this paper, we propose a framework called GroupTalk to learn multiple image caption distributions simultaneously and effectively mimic the diversity of the image captions written by human beings. In particular, a novel iterative update strategy is proposed to separate training sentence samples into groups and learn their distributions at the same time. Furthermore, we introduce an efficient classifier to solve the problem brought about by the non-linear and discontinuous nature of language distributions which will impair performance. Experiments on several benchmark datasets show that GroupTalk naturally diversifies the generated captions of each image without sacrificing the accuracy.

IJCAI Conference 2016 Conference Paper

Self-Paced Boost Learning for Classification

• Te Pi
• Xi Li
• Zhongfei Zhang
• Deyu Meng
• Fei Wu
• Jun Xiao
• Yueting Zhuang

Effectiveness and robustness are two essential aspects of supervised learning studies. For effective learning, ensemble methods are developed to build a strong effective model from ensemble of weak models. For robust learning, self-paced learning (SPL) is proposed to learn in a self-controlled pace from easy samples to complex ones. Motivated by simultaneously enhancing the learning effectiveness and robustness, we propose a unified framework, Self-Paced Boost Learning (SPBL). With an adaptive from-easy-to-hard pace in boosting process, SPBL asymptotically guides the model to focus more on the insufficiently learned samples with higher reliability. Via a max-margin boosting optimization with self-paced sample selection, SPBL is capable of capturing the intrinsic inter-class discriminative patterns while ensuring the reliability of the samples involved in learning. We formulate SPBL as a fully-corrective optimization for classification. The experiments on several real-world datasets show the superiority of SPBL in terms of both effectiveness and robustness.

AAAI Conference 2015 Conference Paper

Metric Learning Driven Multi-Task Structured Output Optimization for Robust Keypoint Tracking

• Liming Zhao
• Xi Li
• Jun Xiao
• Fei Wu
• Yueting Zhuang

As an important and challenging problem in computer vision and graphics, keypoint-based object tracking is typically formulated in a spatio-temporal statistical learning framework. However, most existing keypoint trackers are incapable of effectively modeling and balancing the following three aspects in a simultaneous manner: temporal model coherence across frames, spatial model consistency within frames, and discriminative feature construction. To address this issue, we propose a robust keypoint tracker based on spatio-temporal multi-task structured output optimization driven by discriminative metric learning. Consequently, temporal model coherence is characterized by multi-task structured keypoint model learning over several adjacent frames, while spatial model consistency is modeled by solving a geometric verification based structured learning problem. Discriminative feature construction is enabled by metric learning to ensure the intra-class compactness and inter-class separability. Finally, the above three modules are simultaneously optimized in a joint learning scheme. Experimental results have demonstrated the effectiveness of our tracker.

YNIMG Journal 2007 Journal Article

Hue maps in primate striate cortex

• Youping Xiao
• Alexander Casti
• Jun Xiao
• Ehud Kaplan

The macaque striate cortex (V1) contains neurons that respond preferentially to various hues. The properties of these hue-selective neurons have been studied extensively at the single-unit level, but it is unclear how stimulus hue is represented by the distribution of activity across neuronal populations in V1. Here we use the intrinsic optical signal to image V1 responses to spatially uniform stimuli of various hues. We found that (1) each of these stimuli activates an array of patches in the supragranular layers of the parafoveal V1; (2) the patches activated by different hues overlapped partially; 3) the peak locations of these patches were determined by stimulus hue. The peaks associated with various hues form well-separated clusters, in which nearby peaks represent perceptually similar hues. Each cluster represents a full gamut of hue in a small cortical area (∼160 μm long). The hue order is preserved within each peak cluster, but the clusters have various geometrical shapes. These clusters were co-localized with regions that responded preferentially to chromatic gratings compared with achromatic ones. Our results suggest that V1 contains an array of hue maps, in which the hue of a stimulus is represented by the location of the peak response to the stimulus. The orderly, organized hue maps in V1, together with the recently discovered hue maps in the extrastriate cortical area V2, are likely to play an important role in hue perception in primates.

IROS Conference 2006 Conference Paper

Research of TDOA Based Self-localization Approach in Wireless Sensor Network

• Jun Xiao
• Lirong Ren
• Jindong Tan

Sensor network, which has integrated wireless communication, date collection and information processing capacities, is a booming technique in information collecting. With the development of research on wireless sensor networks (WSNs), localization in the WSNs has become a very important research point. At present, there are mainly two kinds of approaches, range-based approach and range-free approach. Localization precision of range-based approach is higher than the range-free approach. In the range-based approach, time difference of arrival (TDOA) method needs less requirements for time synchronization. This paper proposes a self-localization approach based on TDOA, which utilizes average value of time difference by rolling average to decrease the measurement error, and adopts unconstrained least squares (LS) estimator to achieve the accurate localization. Simulation results and error analysis prove its validity

AAMAS Conference 2004 Conference Paper

An Empirical Study of the Effect of Agent Competence on User Performance and Perception

• Jun Xiao
• John Stasko
• Richard Catrambone

ICRA Conference 2004 Conference Paper

Fuzzy System Approach for Task Planning and Control of Micro Wall Climbing Robots

• Jun Xiao
• Jizhong Xiao
• Ning Xi 0001
• Weihua Sheng

This paper presents a fuzzy system approach that incorporates sensing, control and planning to enable micro wall climbing robots navigating in unstructured environments. Based on a fuzzy multi-sensor data fusion scheme, a novel strategy for integrating task scheduling, sensing, planning and real-time execution is proposed so that task scheduling, action planning and motion control can be treated in a unified framework, and the design of task synchronization becomes much easier. A hybrid method is employed to create robot's gaits by switching the robot between continuous motion modes with the help of a finite state machine driven by the signals from robot sensors. For a highly non-linear system as the micro wall climbing robot, a fuzzy motion controller is designed to improve the robot's performance at different situations. Experimental results prove the validity of the proposed method.

IROS Conference 2003 Conference Paper

Multi-sensor referenced gait control of a miniature climbing robot

• Jizhong Xiao
• Ning Xi 0001
• Jun Xiao
• Jindong Tan

This paper describes a gait generation and control approach of a bipedal climbing robot with under-actuated mechanism. The special mechanical structure enables the robot to perform exploration tasks using "crawling", "pivoting" or "climbing" gaits. Multiple sensors are synthesized to generate successful gaits using a finite state machine. Experiments are conducted which demonstrate the effectiveness of proposed approach.