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arXiv 2024年

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Wang, Yifan Luo, Xiao Zhang, Ming School of Computer Science National Key Laboratory for Multimedia Information Processing Anker Embodied AI Lab Peking University China School of Statistics and Data Science Nankai University China Department of Computer Science University of California Los Angeles United States School of Information Technology & Management University of International Business and Economics China

In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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A Multi-input Deep neural Network Framework for Non-invasive Detection of Anemia using Finger Nail Images

A Multi-input Deep Neural Network Framework for Non-invasive...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Krithika S Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Anemia, characterized by a deficiency in red blood corpuscles or hemoglobin, poses a significant global health challenge, particularly affecting vulnerable populations. Traditional diagnostic methods often involve invasive procedures, posing challenges in resource-limited settings. This study aims to explore non-invasive anemia detection using fingernail images and convolutional neural networks (CNNs) as a promising alternative to conventional diagnostic approaches. The study utilizes a dataset of fingernail images collected from hospitals in Ghana, comprising both anemic and non-anemic cases. The dataset undergoes preprocessing, including selective enhancement of red components, conversion to the CIElab color space, and feature extraction. A multi-input Deep neural Network (DNN) framework employing pre-trained CNNs is proposed for anemia classification. The pre-trained CNN architectures include EfficientNet B1, EfficientNet B4, and MobileNet V3. The framework’s performance was assessed using two methodologies: The first involved random shuffling of the dataset, followed by division into training, testing, and validation sets, with evaluation metrics including Accuracy, Precision, F1 scores, and a Confusion Matrix. The second employed five-fold cross-validation, measured using accuracy. The evaluation of the proposed DNN framework using both of the methodologies indicates that EfficientNet B4 achieved the highest testing accuracy (97.87%), precision (97.88%), recall (97.87%), and F1 score (97.88%) and a cross-validation accuracy of 97.37% for the first and second methodologies respectively making it best fit for the proposed DNN framework. The findings demonstrate that the proposed framework yields promising results, especially under the second approach, and opens avenues for further exploration in transfer learning, fine-tuning of deep neural networks for multi-input feature integration, and cross-validation.

关键词： Training Accuracy Transfer learning Sociology Nails Quality assessment Convolutional neural networks

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Hypergraph Consistency Learning with Relational Distillation

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IEEE Transactions on Multimedia 2025年

作者： Yi, Siyu Mao, Zhengyang Wang, Yifan Gu, Yiyang Xiao, Zhiping Chen, Chong Hua, Xian-Sheng Zhang, Ming Ju, Wei Sichuan University College of Mathematics Chengdu610065 China Peking University-Anker Embodied AI Lab Peking University School of Computer Science National Key Laboratory for Multimedia Information Processing Beijing100871 China University of International Business and Economics School of Information Technology & Management Beijing100029 China University of Washington Department of Computer Science Seattle United States Terminus Group Beijing100027 China Sichuan University College of Computer Science Chengdu610065 China

This paper studies the problem of semi-supervised learning on graphs, which has recently aroused widespread interest in relational data mining. The focal point of exploration in this area has been the utilization of graph neural networks (GNNs), which stand out for excellent performance. Previous methods, however, typically rely on the limited labeled data while ignoring the abundant structural information in unlabeled nodes inherently on graphs, easily resulting in overfitting, especially in scenarios where only a few label nodes are available. Even worse, GNNs, despite their success, are constrained by their ability to solely capture local neighborhood information through message-passing mechanisms, thereby falling short in modeling higher-order dependencies among nodes. To circumvent the above drawbacks, we propose a simple yet effective framework called Hypergraph COnsistency LeArning (HOLA). Specifically, we employ a collaborative distillation framework consisting of a teacher network and a student network. To achieve effective interaction, we propose momentum distillation, a self-training method that enables the student network to learn from pseudo-targets generated by a momentum teacher network. Further, a novel hypergraph structure learning network is developed to model complex high-order relations among nodes with relational consistency learning, thereby transferring the knowledge to the student network. Extensive experiments conducted on a variety of benchmark datasets demonstrate the superior performance of the HOLA over various state-of-the-art methods. © 2025 IEEE.

关键词： Self-supervised learning

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RGBManip: Monocular Image-based Robotic Manipulation through Active Object Pose Estimation

RGBManip: Monocular Image-based Robotic Manipulation through...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Boshi An Yiran Geng Kai Chen Xiaoqi Li Qi Dou Hao Dong Hyperplane Lab School of CS Peking University and National Key Laboratory for Multimedia Information Processing Department of Computer Science and Engineering The Chinese University of Hong Kong Beijing Academy of Artificial Intelligence (BAAI)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Robotic manipulation requires accurate perception of the environment, which poses a significant challenge due to its inherent complexity and constantly changing nature. In this context, RGB image and point-cloud observations are two commonly used modalities in visual-based robotic manipulation, but each of these modalities have their own limitations. Commercial point-cloud observations often suffer from issues like sparse sampling and noisy output due to the limits of the emission-reception imaging principle. On the other hand, RGB images, while rich in texture information, lack essential depth and 3D information crucial for robotic manipulation. To mitigate these challenges, we propose an image-only robotic manipulation framework that leverages an eye-on-hand monocular camera installed on the robot’s parallel gripper. By moving with the robot gripper, this camera gains the ability to actively perceive the object from multiple perspectives during the manipulation process. This enables the estimation of 6D object poses, which can be utilized for manipulation. While, obtaining images from more and diverse viewpoints typically improves pose estimation, it also increases the manipulation time. To address this trade-off, we employ a reinforcement learning policy to synchronize the manipulation strategy with active perception, achieving a balance between 6D pose accuracy and manipulation efficiency. Our experimental results in both simulated and real-world environments showcase the state-of-the-art effectiveness of our approach. We believe that our method will inspire further research on real-world-oriented robotic manipulation. See https://***/ for more details.

关键词： Accuracy Three-dimensional displays Pose estimation Robot vision systems Cameras Sensors Synchronization

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

Meta-ZSDETR: Zero-shot DETR with Meta-learning

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International Conference on computer Vision (ICCV)

作者： Lu Zhang Chenbo Zhang Jiajia Zhao Jihong Guan Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of existing works face two problems: the low recall of RPN in unseen classes and the confusion of unseen classes with background. In this paper, we present the first method that combines DETR and meta-learning to perform zero-shot object detection, named Meta-ZSDETR, where model training is formalized as an individual episode based meta-learning task. Different from Faster R-CNN based methods that firstly generate class-agnostic proposals, and then classify them with visual-semantic alignment module, Meta-ZSDETR directly predict class-specific boxes with class-specific queries and further filter them with the predicted accuracy from classification head. The model is optimized with meta-contrastive learning, which contains a regression head to generate the coordinates of class-specific boxes, a classification head to predict the accuracy of generated boxes, and a contrastive head that utilizes the proposed contrastive-reconstruction loss to further separate different classes in visual space. We conduct extensive experiments on two benchmark datasets MS COCO and PASCAL VOC. Experimental results show that our method outperforms the existing ZSD methods by a large margin.

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Meta-ZSDETR: Zero-shot DETR with Meta-learning

arXiv

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arXiv 2023年

作者： Zhang, Lu Zhang, Chenbo Zhao, Jiajia Guan, Jihong Zhou, Shuigeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China Department of Computer Science & Technology Tongji University China

关键词： Object detection

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Vocabulary-informed Zero-shot and Open-set Learning

arXiv

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arXiv 2023年

作者： Fu, Yanwei Wang, Xiaomei Dong, Hanze Jiang, Yu-Gang Wang, Meng Xue, Xiangyang Sigal, Leonid The School of Data Science Fudan University Shanghai China The School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University China AITRICS Korea Republic of The School of Computer and Information Science Hefei University of Technology Hefei China The Department of Computer Science University of British Columbia BC Canada

Despite significant progress in object categorization, in recent years, a number of important challenges remain;mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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SMI-EDITOR: EDIT-BASED SMILES LANGUAGE MODEL WITH FRAGMENT-LEVEL SUPERVISION

arXiv

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arXiv 2024年

作者： Zheng, Kangjie Liang, Siyue Yang, Junwei Feng, Bin Liu, Zequn Ju, Wei Xiao, Zhiping Zhang, Ming School of Computer Science Peking University China National Key Laboratory for Multimedia Information Processing Peking University China Peking University Anker Embodied AI Lab Peking University China Shenzhen China College of Computer Science Sichuan University Chengdu China Computer Science and Engineering Department University of Washington United States

SMILES, a crucial textual representation of molecular structures, has garnered significant attention as a foundation for pre-trained language models (LMs). However, most existing pre-trained SMILES LMs focus solely on the single-token level supervision during pre-training, failing to fully leverage the substructural information of molecules. This limitation makes the pre-training task overly simplistic, preventing the models from capturing richer molecular semantic information. Moreover, during pre-training, these SMILES LMs only process corrupted SMILES inputs, never encountering any valid SMILES, which leads to a train-inference mismatch. To address these challenges, we propose SMI-EDITOR, a novel edit-based pre-trained SMILES LM. SMI-EDITOR disrupts substructures within a molecule at random and feeds the resulting SMILES back into the model, which then attempts to restore the original SMILES through an editing process. This approach not only introduces fragment-level training signals, but also enables the use of valid SMILES as inputs, allowing the model to learn how to reconstruct complete molecules from these incomplete structures. As a result, the model demonstrates improved scalability and an enhanced ability to capture fragment-level molecular information. Experimental results show that SMI-EDITOR achieves state-of-the-art performance across multiple downstream molecular tasks, and even outperforming several 3D molecular representation models. © 2024, CC BY-NC-SA.

关键词： Semantics

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Survey on depth and RGB image-based 3D hand shape and pose estimation

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Virtual Reality & Intelligent Hardware 2021年第3期3卷 207-234页

作者： Lin HUANG Boshen ZHANG Zhilin GUO Yang XIAO Zhiguo CAO Junsong YUAN Department of Computer Science and Engineering State University of New York at BuffaloNY 14260USA YouTu Lab TencentShanghai 201101China Department of Computer Science Fu Foundation School of Engineering and Applied ScienceColumbia UniversityNY 10027USA National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

The field of vision-based human hand three-dimensional(3D)shape and pose estimation has attracted significant attention recently owing to its key role in various applications,such as natural human computer *** the availability of large-scale annotated hand datasets and the rapid developments of deep neural networks(DNNs),numerous DNN-based data-driven methods have been proposed for accurate and rapid hand shape and pose ***,the existence of complicated hand articulation,depth and scale ambiguities,occlusions,and finger similarity remain *** this study,we present a comprehensive survey of state-of-the-art 3D hand shape and pose estimation approaches using RGB-D *** RGB-D cameras,hand datasets,and a performance analysis are also discussed to provide a holistic view of recent *** also discuss the research potential of this rapidly growing field.

关键词： Hand survey 3D hand pose estimation Hand shape reconstruction Hand-object interactions RGB-D cameras

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