IEEE Transactions on Pattern Analysis and Machine Intelligence最新文献

{"title":"Incorporating Pre-Training Data Matters in Unsupervised Domain Adaptation","authors":"Yinsong Xu, Aidong Men, Yang Liu, Xiahai Zhuang, Qingchao Chen","doi":"10.1109/tpami.2025.3572963","DOIUrl":"https://doi.org/10.1109/tpami.2025.3572963","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"141 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Channel Disentangled Graph Neural Networks with Different Types of Self-constraints","authors":"Zhuomin Liang, Liang Bai, Xian Yang, Jiye Liang","doi":"10.1109/tpami.2025.3572846","DOIUrl":"https://doi.org/10.1109/tpami.2025.3572846","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"7 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Accelerated Off-Policy Stochastic Preconditioned TD(0) Algorithm","authors":"Weidong Liu, Jiahua Ma, Xiaojun Mao, Kejie Tang","doi":"10.1109/tpami.2025.3572807","DOIUrl":"https://doi.org/10.1109/tpami.2025.3572807","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"46 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisit Weakly Supervised Hashing with Deep Multi-modal Foundation Models","authors":"Min Wang, Wengang Zhou, Houqiang Li","doi":"10.1109/tpami.2025.3573186","DOIUrl":"https://doi.org/10.1109/tpami.2025.3573186","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"42 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Partial Distribution Matching via Partial Wasserstein Adversarial Networks","authors":"Zi-Ming Wang, Nan Xue, Ling Lei, Rebecka Jörnsten, Gui-Song Xia","doi":"10.1109/tpami.2025.3572795","DOIUrl":"https://doi.org/10.1109/tpami.2025.3572795","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"33 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QDNet: Query-Denoising Network for Visual Traffic Knowledge Graph Generation","authors":"Yunfei Guo, Fei Yin, Xiao-Hui Li, Cheng-Lin Liu","doi":"10.1109/tpami.2025.3572944","DOIUrl":"https://doi.org/10.1109/tpami.2025.3572944","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"31 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates","authors":"Daniele Angioni, Luca Demetrio, Maura Pintor, Luca Oneto, Davide Anguita, Battista Biggio, Fabio Roli","doi":"10.1109/tpami.2025.3573237","DOIUrl":"https://doi.org/10.1109/tpami.2025.3573237","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"22 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-rigid Point Cloud Registration via Anisotropic Hybrid Field Harmonization.","authors":"Jinyang Wang,Xuequan Lu,Mohammed Bennamoun,Bin Sheng","doi":"10.1109/tpami.2025.3572584","DOIUrl":"https://doi.org/10.1109/tpami.2025.3572584","url":null,"abstract":"Current point cloud registration algorithms struggle to effectively handle both deformations and occlusions simultaneously. Our manifold analysis reveals this limitation arises from the inaccurate modeling of the shape's underlying manifold and the lack of an effective optimization strategy for fragmented manifold structures. In this paper, we present AniSym-Net, a novel non-rigid registration framework designed to address near-isometric deformation registration in the presence of occlusions. To encode object's coarse topological properties and local geometric information, AniSym-Net introduces a novel anisotropic hybrid shape-motion deformation field. The effectiveness of the anisotropic hybrid shape-motion fields relies on both the holonomic constraints from the symplectic structure modeling in AniSym-Net and the motion-conditional cross-attention during fusion, which calibrates geometric features using velocity-boundary constrained point motion patterns. The harmonization of correspondences derived from anisotropic hybrid fields and those from motion-shape fields significantly mitigates registration errors and occlusions. This is achieved through the optimization of loop closures of cotangent bundles within the symplectic manifold framework. We conduct comprehensive evaluation across five popular benchmarks, namely CAPE, DT4D, SAPIEN, FAUST, and DeepDeform, to demonstrate our AniSym-Net's superior performance compared to the state-of-the-art methods.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"78 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long Short-Term Knowledge Decomposition and Consolidation for Lifelong Person Re-Identification.","authors":"Kunlun Xu,Zichen Liu,Xu Zou,Yuxin Peng,Jiahuan Zhou","doi":"10.1109/tpami.2025.3572468","DOIUrl":"https://doi.org/10.1109/tpami.2025.3572468","url":null,"abstract":"Lifelong person re-identification (LReID) aims to learn from streaming data sources step by step, which suffers from the catastrophic forgetting problem. In this paper, we investigate the exemplar-free LReID setting where no previous exemplar is available during the new step training. Existing exemplar-free LReID methods primarily adopt knowledge distillation to transfer knowledge from an old model to a new one without selection, inevitably introducing erroneous and detrimental information that hinders new knowledge learning. Furthermore, not all critical knowledge can be transferred due to the absence of old data, leading to the permanent loss of undistilled knowledge. To address these limitations, we propose a novel exemplar-free LReID method named Long Short-Term Knowledge Decomposition and Consolidation (LSTKC++). Specifically, an old knowledge rectification mechanism is developed to rectify the old model predictions based on new data annotations, ensuring correct knowledge transfer. Besides, a long-term knowledge consolidation strategy is designed, which first estimates the degree of old knowledge forgetting by leveraging the output difference between the old and new models. Then, a knowledge-guided parameter fusion strategy is developed to balance new and old knowledge, improving long-term knowledge retention. Upon these designs, considering LReID models tend to be biased on the latest seen domains, the fusion weights generated by this process often lead to sub-optimal knowledge balancing. To settle this, we further propose to decompose a single old model into two parts: a long-term old model containing multi-domain knowledge and a short-term model focusing on the latest short-term old knowledge. Then, the incoming new data are explored as an unbiased reference to adjust the old models' fusion weight to achieve backward optimization. Furthermore, an extended complementary knowledge rectification mechanism is developed to mine and retain the correct knowledge in the decomposed models. Extensive experimental results demonstrate that LSTKC++ significantly outperforms state-of-the-art methods by large margins. The code is available at https://github.com/zhoujiahuan1991/LSTKC +.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"33 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144122159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SceneTracker: Long-Term Scene Flow Estimation Network","authors":"Bo Wang, Jian Li, Yang Yu, Li Liu, Zhenping Sun, Dewen Hu","doi":"10.1109/tpami.2025.3572489","DOIUrl":"https://doi.org/10.1109/tpami.2025.3572489","url":null,"abstract":"","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"33 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}