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GAEM: Graph-driven Attention-based Entropy Model for LiDAR Point Cloud Compression

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cui, Mingyue Zhong, Yuyang Feng, Mingjian Long, Junhua Ling, Yehua Xu, Jiahao Huang, Kai Sun Yat-sen University School of Computer Science and Engineering Guangzhou400100 China Sun Yat-sen University key Laboratory of Machine Intelligence and Advanced Computing Guangzhou400100 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Guangxi Transportation Science and Technology Group Co. Ltd Nanning530029 China

High-quality LiDAR point cloud (LPC) coding is essential for efficiently transmitting and storing the vast amounts of data required for accurate 3D environmental representation. The Octree-based entropy coding framework has emerged as the predominant method, however, previous study usually overly relies on large-scale attention-based context prediction to encode Octree nodes, overlooking the inherent correlational properties of this structure. In this paper, we propose a novel Graph-driven Attention-based Entropy Model (GAEM), which adopts partitioned graph attention mechanisms to uncover contextual dependencies among neighboring nodes. Different from the Cartesian coordinate-based coding mode with higher redundancy, GAEM uses the multi-level spherical Octree to organize point clouds, improving the quality of LPC reconstruction. GAEM combines graph convolution for node feature embedding and grouped-graph attention for exploiting dependency among contexts, which preserves performance in low-computation using localized nodes. Besides, to further increase the receptive field, we design a high-resolution cross-attention module introducing sibling nodes. Experimental results show that our method achieves state-of-the-art performance on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford, compared to all baselines. Compared to the benchmark GPCC, our method achieves gains of up to 53.9% and 53.6% on SemanticKITTI and Ford while compared to the sibling-introduced methods, we achieve up to 42.3% and 44.7% savings in encoding/decoding time. In particular, our GAEM allows for extension to downstream tasks (i.e., vehicle detection and semantic segmentation), further demonstrating the practicality of the method. (Figure presented) © 1991-2012 IEEE.

关键词： Semantic Segmentation

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ThicknessVAE: Learning a Lateral Prior for Clothed Human Body Reconstruction

ThicknessVAE: Learning a Lateral Prior for Clothed Human Bod...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaotao Wu Zhaoxin Fan Huiguang He Dinggang Shen School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China NeuBCI Group State Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology Institute of Automation Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Institute of Artificial Intelligence Beihang University Beijing China Beijing Academy of Blockchain and Edge Computing China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Sandwich-like structures have shown remarkable efficacy in clothed human reconstruction. However, these approaches often generate unrealistic side geometries due to inadequate handling of lateral regions. This paper addresses this limitation by incorporating the side geometry of clothed humans as a prior. We propose ThicknessVAE, a novel two-stage method that makes two key contributions: (1) We learn a prototype from point clouds for the lateral regions of clothed humans to extract common and detailed geometric features. (2) We utilize this prototype as a prior to transform geometric features into a thickness map associated with clothed human images, enabling refined normal integration for sandwich-like reconstruction methods. By seamlessly integrating our model into the sandwich-like reconstruction pipeline, we achieve highly realistic side views. Both qualitative and quantitative experiments demonstrate that our approach is comparable to state-of-the-art methods in terms of side-view realism.

关键词： Geometry Surface reconstruction Three-dimensional displays Pipelines Prototypes Transforms Feature extraction Speech processing Image reconstruction Surface treatment

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Progressive Human Motion Generation Based on Text and Few Motion Frames

arXiv

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arXiv 2025年

作者： Zeng, Ling-An Wu, Gaojie Wu, Ancong Hu, Jian-Fang Zheng, Wei-Shi School of Artificial Intelligence Sun Yat-sen University Guangdong Zhuhai519082 China School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou510275 China School of Computer Science and Engineering Sun Yat-sen University China The Guangdong Key Laboratory of Information Security Technology China The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Peng Cheng Laboratory China

Although existing text-to-motion (T2M) methods can produce realistic human motion from text description, it is still difficult to align the generated motion with the desired postures since using text alone is insufficient for precisely describing diverse postures. To achieve more controllable generation, an intuitive way is to allow the user to input a few motion frames describing precise desired postures. Thus, we explore a new Text-Frame-to-Motion (TF2M) generation task that aims to generate motions from text and very few given frames. Intuitively, the closer a frame is to a given frame, the lower the uncertainty of this frame is when conditioned on this given frame. Hence, we propose a novel Progressive Motion Generation (PMG) method to progressively generate a motion from the frames with low uncertainty to those with high uncertainty in multiple stages. During each stage, new frames are generated by a Text-Frame Guided Generator conditioned on frame-aware semantics of the text, given frames, and frames generated in previous stages. Additionally, to alleviate the train-test gap caused by multi-stage accumulation of incorrectly generated frames during testing, we propose a Pseudo-frame Replacement Strategy for training. Experimental results show that our PMG outperforms existing T2M generation methods by a large margin with even one given frame, validating the effectiveness of our PMG. Code is available here. Copyright © 2025, The Authors. All rights reserved.

关键词： Human engineering

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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A New Neurodynamics-Based Model-Less Fault-Tolerant Scheme for Redundant Robot Motion Planning and Control

SSRN

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SSRN 2023年

作者： Wang, Xin Tan, Ning Zhong, Zhaohui Huang, Kai School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou China

For tasks utilizing redundant manipulators, there involves motion of a number of joints when performing tracking control. In some cases, one or a few of joints might fail and then result in task failure or even damage, which means that fault tolerance is an indispensable capability for robot control systems. In this paper, we propose a novel fault-tolerant scheme to endow the redundant robots with this capability in the condition that certain joints are not working. More importantly, our scheme can achieve the fault-tolerant tracking control in a data-driven manner based on zeroing neurodyanmics when the robot kinematic model is unavailable, which is much different from existing fault-tolerant control algorithms of redundant robots that require to the kinematic model in advance. The proposed model-less fault tolerant tracking control (MLFC) scheme considers the fault-tolerant control problem of redundant manipulator as a quadratic programming (QP) problem. Then, with the help of a discrete solver proposed in this paper, the fault-tolerant control problem is transformed into a unified computing problem in the condition that without knowing the redundant manipulator's model. Extensive simulations and experiments based on a six degrees of freedom (DOF) redundant manipulator are analyzed the verification of our scheme. © 2023, The Authors. All rights reserved.

关键词： Fault tolerance

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Inner-outer aware reconstruction model for monocular 3D scene reconstruction 23

Inner-outer aware reconstruction model for monocular 3D scen...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yu-Kun Qiu Guo-Hao Xu Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China School of Computer Science and Engineering Sun Yat-sen University China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Monocular 3D scene reconstruction aims to reconstruct the 3D structure of scenes based on posed images. Recent volumetric-based methods directly predict the truncated signed distance function (TSDF) volume and have achieved promising results. The memory cost of volumetric-based methods will grow cubically as the volume size increases, so a coarse-to-fine strategy is necessary for saving memory. Specifically, the coarse-to-fine strategy distinguishes surface voxels from non-surface voxels, and only potential surface voxels are considered in the succeeding procedure. However, the non-surface voxels have various features, and in particular, the voxels on the inner side of the surface are quite different from those on the outer side since there exists an intrinsic gap between them. Therefore, grouping inner-surface and outer-surface voxels into the same class will force the classifier to spend its capacity to bridge the gap. By contrast, it is relatively easy for the classifier to distinguish inner-surface and outer-surface voxels due to the intrinsic gap. Inspired by this, we propose the inner-outer aware reconstruction (IOAR) model. IOAR explores a new coarse-to-fine strategy to classify outer-surface, inner-surface and surface voxels. In addition, IOAR separates occupancy branches from TSDF branches to avoid mutual interference between them. Since our model can better classify the surface, outer-surface and inner-surface voxels, it can predict more precise meshes than existing methods. Experiment results on ScanNet, ICL-NUIM and TUM-RGBD datasets demonstrate the effectiveness and generalization of our model. The code is available at https://***/YorkQiu/InnerOuterAwareReconstruction.

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Diversifying spatial-temporal perception for video domain generalization 23

Diversifying spatial-temporal perception for video domain ge...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Kun-Yu Lin Jia-Run Du Yipeng Gao Jiaming Zhou Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China School of Computer Science and Engineering Sun Yat-sen University China and Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Video domain generalization aims to learn generalizable video classification models for unseen target domains by training in a source domain. A critical challenge of video domain generalization is to defend against the heavy reliance on domain-specific cues extracted from the source domain when recognizing target videos. To this end, we propose to perceive diverse spatial-temporal cues in videos, aiming to discover potential domain-invariant cues in addition to domain-specific cues. We contribute a novel model named Spatial-Temporal Diversification Network (STDN), which improves the diversity from both space and time dimensions of video data. First, our STDN proposes to discover various types of spatial cues within individual frames by spatial grouping. Then, our STDN proposes to explicitly model spatial-temporal dependencies between video contents at multiple space-time scales by spatial-temporal relation modeling. Extensive experiments on three benchmarks of different types demonstrate the effectiveness and versatility of our approach.

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CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models 39

CoMT: A Novel Benchmark for Chain of Multi-modal Thought on ...

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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Cheng, Zihui Chen, Qiguang Zhang, Jin Fei, Hao Feng, Xiaocheng Che, Wanxiang Li, Min Qin, Libo School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for SCIR Harbin Institute of Technology Harbin China National University of Singapore Singapore

ISBN: (纸本)157735897X

Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning. Despite these successes, current benchmarks still follow a traditional paradigm with multi-modal input and text-modal output, which leads to significant drawbacks such as missing visual operations and vague expressions. Motivated by this, we introduce a novel Chain of Multi-modal Thought (CoMT) benchmark to address these limitations. Different from the traditional MCoT benchmark, CoMT requires both multi-modal input and multi-modal reasoning output, aiming to mimic human-like reasoning that inherently integrates visual operations. Specifically, CoMT consists of four categories: (1) Visual Creation, (2) Visual Deletion, (3) Visual Update, and (4) Visual Selection to comprehensively explore complex visual operations and concise expression in real scenarios. We evaluate various LVLMs and strategies on CoMT, revealing some key insights into the capabilities and limitations of the current approaches. We hope that CoMT can inspire more breakthroughs on introducing multi-modal generation into the reasoning process. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Modal analysis

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Facial Expression Recognition With Vision Transformer Using Fused Shifted Windows

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IEEE Transactions on Affective computing 2024年

作者： Sun, Xiao Wang, Rui Chen, Shaokai Wang, Meng Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machines School of Computer Science and Information Engineering China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China University of Science and Technology of China Institute of Advanced Technology China Hefei University of Technology School of Computer Science and Information Engineering Hefei China

Facial expressions contain massive affective information. Previous methods have focused on using diverse models based on CNN or Transformer to handle the facial expression recognition(FER) task. However, most of them treat the FER task as a general image classification task and neglect the impact of regions of interest (ROIs) for the performance of FER. To verify the influence of different ROIs, in this paper, we propose a vision Transformer based on Fused Shifted windows (FSwin), called FSwin Transformer. The semantic information of the face, obtained by ROIs, guides the FSwin Transformer to focus more on the key regions. The fused shifted windows enable the model to perform global semantic interactions, allowing it to concentrate on key regions without losing the topological structural information of the entire face. Additionally, learnable parameters are introduced to learn the feature weights expressed by each ROI, helping the model dynamically adjust the attention distribution. We have conducted controlled experiments to quantitatively verify the impact of ROIs. And the experimental results show that with the increase of the number of ROIs, the accuracy of FER is significantly improved, demonstrating that the key ROIs play an important role in feature extraction. The results on Jaffe, CK+, FER2013, AffectNet have reached 99.8%, 99.0%, 74.8%, and 68.9%, respectively, which all set new state-of-the-art. Extensive cross-dataset experiments also show that the FSwin Transformer has good generalization ability, proving that our proposed model has a beneficial effect on FER tasks. © 2010-2012 IEEE.

关键词： Feature extraction

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CONFIDENCE-GUIDED ADAPTIVE GATE AND DUAL DIFFERENTIAL ENHANCEMENT FOR VIDEO SALIENT OBJECT DETECTION

CONFIDENCE-GUIDED ADAPTIVE GATE AND DUAL DIFFERENTIAL ENHANC...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Chen, Peijia Lai, Jianhuang Wang, Guangcong Zhou, Huajun School of Computer Science and Engineering Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781665438643

Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences. However, spatial and temporal cues are often unreliable in real-world scenarios, such as low-contrast foreground, fast motion, and multiple moving objects. To address these problems, we propose a new framework to adaptively capture available information from spatial and temporal cues, which contains Confidence-guided Adaptive Gate (CAG) modules and Dual Differential Enhancement (DDE) modules. For both RGB features and optical flow features, CAG estimates confidence scores supervised by the IoU between predictions and the ground truths to re-calibrate the information with a gate mechanism. DDE captures the differential feature representation to enrich the spatial and temporal information and generate the fused features. Experimental results on four widely used datasets demonstrate the effectiveness of the proposed method against thirteen state-of-the-art methods. © 2021 IEEE

关键词： Object detection

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