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arXiv 2024年

作者： Jiao, Pengkun Zhao, Na Chen, Jingjing Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Singapore University of Technology and Design Singapore

Open-vocabulary 3D object detection (OV-3DDet) aims to localize and recognize both seen and previously unseen object categories within any new 3D scene. While language and vision foundation models have achieved success in handling various open-vocabulary tasks with abundant training data, OV-3DDet faces a significant challenge due to the limited availability of training data. Although some pioneering efforts have integrated vision-language models (VLM) knowledge into OV-3DDet learning, the full potential of these foundational models has yet to be fully exploited. In this paper, we unlock the textual and visual wisdom to tackle the open-vocabulary 3D detection task by leveraging the language and vision foundation models. We leverage a vision foundation model to provide image-wise guidance for discovering novel classes in 3D scenes. Specifically, we utilize a object detection vision foundation model to enable the zero-shot discovery of objects in images, which serves as the initial seeds and filtering guidance to identify novel 3D objects. Additionally, to align the 3D space with the powerful vision-language space, we introduce a hierarchical alignment approach, where the 3D feature space is aligned with the vision-language feature space using a pre-trained VLM at the instance, category, and scene levels. Through extensive experimentation, we demonstrate significant improvements in accuracy and generalization, highlighting the potential of foundation models in advancing open-vocabulary 3D object detection in real-world scenarios. © 2024, CC BY.

关键词： Object detection

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Discrete Curvature Graph Information Bottleneck 39

Discrete Curvature Graph Information Bottleneck

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fu, Xingcheng Wang, Jian Gao, Yisen Sun, Qingyun Yuan, Haonan Li, Jianxin Li, Xianxian Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China Institute of Artificial Intelligence Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)157735897X

Graph neural networks(GNNs) have been demonstrated to depend on whether the node effective information is sufficiently passing. Discrete curvature (Ricci curvature) is used to study graph connectivity and information propagation efficiency with a geometric perspective, and has been raised in recent years to explore the efficient message-passing structure of GNNs. However, most empirical studies are based on directly observed graph structures or heuristic topological assumptions and lack in-depth exploration of underlying optimal information transport structures for downstream tasks. We suggest that graph curvature optimization is more in-depth and essential than directly rewiring or learning for graph structure with richer message-passing characterization and better information transport interpretability. From both graph geometry and information theory perspectives, we propose the novel Discrete Curvature Graph Information Bottleneck (CurvGIB) framework to optimize the information transport structure and learn better node representations simultaneously. CurvGIB advances the Variational Information Bottleneck (VIB) principle for Ricci curvature optimization to learn the optimal information transport pattern for specific downstream tasks. The learned Ricci curvature is used to refine the optimal transport structure of the graph, and the node representation is fully and efficiently learned. Moreover, to reduce the computational complexity of Ricci curvature, we combine Ricci flow and VIB to deduce a curvature optimization approximation to form a tractable IB objective function. Extensive experiments on various datasets demonstrate the superior effectiveness and interpretability of CurvGIB. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

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Intelligent Automation & Soft Computing 2024年第4期39卷 725-744页

作者： Jielin Jiang Chao Cui Xiaolong Xu Yan Cui School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing210023China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing210044China College of Mathematics and Information Science Nanjing Normal University of Special EducationNanjing210038China

In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric ***,identifying fabric defects forms a crucial part of the fabric production *** fabric defect detection algorithms can only detect specific materials and specific fabric defect types;in addition,their detection efficiency is low,and their detection results are relatively *** learning-based methods have many advantages in the field of fabric defect detection,however,such methods are less effective in identifying multiscale fabric defects and defects with complex ***,we propose an effective algorithm,namely multilayer feature extraction combined with deformable convolution(MFDC),for fabric defect *** MFDC,multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric *** this basis,a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric *** this approach,Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned *** experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes,at the expense of a small increase in detection time.

关键词： Fabric defect detection multi-layer features deformable convolution

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Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective

arXiv

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arXiv 2024年

作者： Li, De Li, Xianxian Gan, Zeming Li, Qiyu Qu, Bin Wang, Jinyan Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

Graph neural networks (GNNs) based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training data. Most existing noisy labeling approaches focus on the visual domain or graph node classification tasks and analyze the impact of noisy labels only from a utility perspective. Unlike existing work, in this paper, we measure the effects of noise labels on graph classification from data privacy and model utility perspectives. We find that noise labels degrade the model’s generalization performance and enhance the ability of membership inference attacks on graph data privacy. To this end, we propose the robust graph neural network (RGLC) approach with noisy labeled graph classification. Specifically, we first accurately filter the noisy samples by high-confidence samples and the first feature principal component vector of each class. Then, the robust principal component vectors and the model output under data augmentation are utilized to achieve noise label correction guided by dual spatial information. Finally, supervised graph contrastive learning is introduced to enhance the embedding quality of the model and protect the privacy of the training graph data. The utility and privacy of the proposed method are validated by comparing twelve different methods on eight real graph classification datasets. Compared with the state-of-the-art methods, the RGLC method achieves at most and at least 7.8% and 0.8% performance gain at 30% noisy labeling rate, respectively, and reduces the accuracy of privacy attacks to below 60%. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification

arXiv

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arXiv 2024年

作者： Han, Xiyu Zhong, Xian Huang, Wenxin Jia, Xuemei Liu, Wenxuan Yu, Xiaohan Kot, Alex Chichung Wuhan University of Technology Sanya Science and Education Innovation Park Sanya572025 China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China School of Computer Science and Information Engineering Hubei University Wuhan430062 China School of Computer Science Wuhan University Wuhan430072 China School of Computer Science Peking University Beijing100091 China School of Computing Macquarie University SydneyNSW2109 Australia Rapid-Rich Object Search Lab School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

Cloth-changing person re-identification (CC-ReID) aims to match individuals across multiple surveillance cameras despite variations in clothing. Existing methods typically focus on mitigating the effects of clothing changes or enhancing ID-relevant features but often struggle to capture complex semantic information. In this paper, we propose a novel prompt learning framework, Semantic Contextual Integration (SCI), for CC-ReID, which leverages the visual-text representation capabilities of CLIP to minimize the impact of clothing changes and enhance ID-relevant features. Specifically, we introduce Semantic Separation Enhancement (SSE) module, which uses dual learnable text tokens to separately capture confounding and clothing-related semantic information, effectively isolating ID-relevant features from distracting clothing semantics. Additionally, we develop a Semantic-Guided Interaction Module (SIM) that uses orthogonalized text features to guide visual representations, sharpening the model’s focus on distinctive ID characteristics. This integration enhances the model’s discriminative power and enriches the visual context with high-dimensional semantic insights. Extensive experiments on three CC-ReID datasets demonstrate that our method outperforms state-of-the-art techniques. The code will be released at github. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Prototypical Residual Networks for Anomaly Detection and Localization

Prototypical Residual Networks for Anomaly Detection and Loc...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hui Zhang Zuxuan Wu Zheng Wang Zhineng Chen Yu-Gang Jiang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Zhejiang University of Technology

Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns; a multisize self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN.

关键词：

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PDA Construction via Union of Cartesian Product Cache Configurations for Coded Caching

arXiv

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arXiv 2025年

作者： Wang, Jinyu Cheng, Minquan Wan, Kai Caire, Giuseppe Key Lab of Education Blockchain and Intelligent Technology Ministry of Education China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin541004 China School of Mathematics and Statistics Guangxi Normal University Guilin541004 China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Electrical Engineering and Computer Science Department Technische Universität Berlin Berlin10587 Germany

Caching is an efficient technique to reduce peak traffic by storing popular content in local caches. Placement delivery array (PDA) proposed by Yan et al. is a combinatorial structure to design coded caching schemes with uncoded placement and one-shot linear delivery. By taking the m-fold Cartesian product of a small base PDA, Wang et al. constructed a big PDA while maintaining the memory ratio and transmission load unchanged, which achieves linear growth in both the number of users and coded caching gain. In order to achieve exponential growth in both the number of users and coded caching gain, in this paper we propose a PDA construction by taking the union operation of the cache configurations from the m-fold Cartesian product of a base PDA. The resulting PDA leads to a coded caching scheme with subpacketization increasing sub-exponentially with the number of users while keeping the load constant for fixed memory ratio. By applying the proposed construction to existing base PDAs, three new coded caching schemes are obtained, which cover some existing schemes as special cases and can achieve lower load with simultaneously lower subpacketization for some memory ratios. Copyright © 2025, The Authors. All rights reserved.

关键词： Cache memory

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Community-Centric Graph Unlearning 39

Community-Centric Graph Unlearning

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Yi Zhang, Shichao Zhang, Guixian Cheng, Debo Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin 541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin 541004 China School of Computer Science and Technology China University of Mining and Technology Jiangsu Xuzhou 221116 China UniSA STEM University of South Australia Mawson Lakes Adelaide Australia

ISBN: (纸本)157735897X

Graph unlearning technology has become increasingly important since the advent of the 'right to be forgotten' and the growing concerns about the privacy and security of artificial intelligence. Graph unlearning aims to quickly eliminate the effects of specific data on graph neural networks (GNNs). However, most existing deterministic graph unlearning frameworks follow a balanced partition-submodel training-aggregation paradigm, resulting in a lack of structural information between subgraph neighborhoods and redundant unlearning parameter calculations. To address this issue, we propose a novel Graph Structure Mapping Unlearning paradigm (GSMU) and a novel method based on it named Community-centric Graph Eraser (CGE). CGE maps community subgraphs to nodes, thereby enabling the reconstruction of a node-level unlearning operation within a reduced mapped graph. CGE makes the exponential reduction of both the amount of training data and the number of unlearning parameters. Extensive experiments conducted on five real-world datasets and three widely used GNN backbones have verified the high performance and efficiency of our CGE method, highlighting its potential in the field of graph unlearning. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Autoregressive Queries for Adaptive Tracking with Spatio-Temporal Transformers

Autoregressive Queries for Adaptive Tracking with Spatio-Tem...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jinxia Xie Bineng Zhong Zhiyi Mo Shengping Zhang Liangtao Shi Shuxiang Song Rongrong Ji Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University School of Computer Science and Technology Harbin Institute of Technology Media Analytics and Computing Lab School of Informatics Xiamen University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The rich spatio-temporal information is crucial to capture the complicated target appearance variations in visual tracking. However, most top-performing tracking algorithms rely on many hand-crafted components for spatio-temporal information aggregation. Consequently, the spatio-temporal information is far away from being fully explored. To alleviate this issue, we propose an adaptive tracker with spatio-temporal transformers (named AQA-Track), which adopts simple autoregressive queries to effectively learn spatio-temporal information without many hand-designed components. Firstly, we introduce a set of learnable and autoregressive queries to capture the instantaneous target appearance changes in a sliding window fashion. Then, we design a novel attention mechanism for the interaction of existing queries to generate a new query in current frame. Finally, based on the initial target template and learnt autoregressive queries, a spatio-temporal information fusion module (STM) is designed for spatiotemporal formation aggregation to locate a target object. Benefiting from the STM, we can effectively combine the static appearance and instantaneous changes to guide robust tracking. Extensive experiments show that our method significantly improves the tracker's performance on six popular tracking benchmarks: LaSOT, LaSOT ext , TrackingNet, GOT-10k, TNL2K, and UAV123. Code and models will be https://***/orgs/GXNU-Zhonglab.

关键词： Adaptation models Visualization computer vision Target tracking Computational modeling Transformers Market research

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PPIRB:Achieving an privacy-preserving image retrieval scheme Based on blockchain 5

PPIRB:Achieving an privacy-preserving image retrieval scheme...

引用

5th International Conference on Data science and Information technology, DSIT 2022

作者： Yuan, Bo Peng, Jiahui Li, Chunpei Qiu, Wangjie School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Guangxi Key Lab of Multi-source Information Mining Security Guangxi Normal University GuiLin China State Key Laboratory of Software Development Environment Beihang University Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

With the proliferation of cloud services, the work of large-scale image retrieval is carried out on remote cloud become a trend in order to get rid of the storage burden and computation. However, traditional retrieval work based on centralized cloud services is considered to take risk in giving away users' privacy in many researches. We need a secure way for retrieving and storing images by decentralized approach. In this paper, we propose a decentralized scheme named PPIRB(An Privacy-Preserving Image Retrieval Scheme Base on blockchain) for retrieving images based on blockchain. We used the data structure called amplifying locality sensitive hashing (ALSH) to improve search accuracy. Searchable symmetric en-cryption(SSE) and ALSH used to realize the encryption retrieval of image index on the blockchain. Our scheme also uses IPFS to replace the cloud server to realize distributed storage, and uses secret sharing scheme to realize the access control. We implemented and evaluated the scheme in Ethereum, and the results show that our scheme is efficient and accurate in finding similar images while preserving privacy. The overall overhead is reasonable. © 2022 IEEE.

关键词： blockchain

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