IEEE Transactions on Pattern Analysis and Machine Intelligence最新文献

Spatial Frequency Modulation for Semantic Segmentation 空间频率调制语义分割

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-24 DOI: 10.1109/tpami.2025.3592621

Linwei Chen, Ying Fu, Lin Gu, Dezhi Zheng, Jifeng Dai

引用次数: 0

Towards Reliable and Faithful Explanations: A Disentanglement-Augmented Approach for Selective Rationalization. 走向可靠和忠实的解释：选择性合理化的解纠缠增强方法。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-24 DOI: 10.1109/tpami.2025.3592313

Linan Yue,Qi Liu,YiChao Du,Li Wang,Yanqing An,Enhong Chen

{"title":"Towards Reliable and Faithful Explanations: A Disentanglement-Augmented Approach for Selective Rationalization.","authors":"Linan Yue,Qi Liu,YiChao Du,Li Wang,Yanqing An,Enhong Chen","doi":"10.1109/tpami.2025.3592313","DOIUrl":"https://doi.org/10.1109/tpami.2025.3592313","url":null,"abstract":"The pursuit of model explainability has prompted the selective rationalization (aka, rationale extraction) which can identify important features (i.e., rationales) from the original input to support prediction results. Existing methods typically involve a cascaded approach with a selector responsible for extracting rationales from the input, followed by a predictor that makes predictions based on the selected rationales. However, these approaches often neglect the information contained in the non-rationales, underutilizing the input. Therefore, in our prior work, we introduce the Disentanglement-Augmented Rationale Extraction (DARE) method, which disentangles the input into rationale and non-rationale components, and enhances rationale representations by minimizing the mutual information between them. While DARE demonstrates strong performance in rationalization, it may still rely on shortcuts in the training distribution, leading to unfaithful rationales. To this end, in this paper, we propose Faith-DARE, an extension of DARE that aims to extract more reliable rationales by mitigating shortcut dependencies. Specifically, we treat the non-rationale features identified by DARE as environments that are decorrelated from the predictions. By shuffling and recombining these environments with rationales, we generate counterfactual samples and identify invariant rationales that remain predictive across shifted distributions. Extensive experiments on graph and textual datasets validate the effectiveness of Faith-DARE. Codes are available at https://github.com/yuelinan/DARE.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"19 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701343","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}

引用次数: 0

An Efficient Image Fusion Network Exploiting Unifying Language and Mask Guidance. 利用统一语言和掩码引导的高效图像融合网络。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-23 DOI: 10.1109/tpami.2025.3591930

Zi-Han Cao,Yu-Jie Liang,Liang-Jian Deng,Gemine Vivone

{"title":"An Efficient Image Fusion Network Exploiting Unifying Language and Mask Guidance.","authors":"Zi-Han Cao,Yu-Jie Liang,Liang-Jian Deng,Gemine Vivone","doi":"10.1109/tpami.2025.3591930","DOIUrl":"https://doi.org/10.1109/tpami.2025.3591930","url":null,"abstract":"Image fusion aims to merge image pairs collected by different sensors over the same scene, preserving their distinct features. Recent works have often focused on designing various image fusion losses, developing different network architectures, and leveraging downstream tasks (e.g., object detection) for image fusion. However, a few studies have explored how language and semantic masks can serve as guidance to aid image fusion. In this paper, we investigate how the combination of language and masks can guide image fusion tasks, discarding the previously complex frameworks, which rely on downstream tasks, GAN-based cycle training, diffusion models, or deep image priors. Additionally, we exploit a recurrent neural network-like architecture to build a lightweight network that avoids the quadratic-cost of traditional attention mechanisms. To adapt the receptance weighted key value (RWKV) model to an image modality, we modify it into a bidirectional version using an efficient scanning strategy (ESS). To guide image fusion by language and mask features, we introduce a multi-modal fusion module (MFM) to facilitate information exchange. Comprehensive experiments show that the proposed framework achieved state-of-the-art results in various image fusion tasks (i.e., visible-infrared image fusion, multi-focus image fusion, multi-exposure image fusion, medical image fusion, hyperspectral and multispectral image fusion, and pansharpening). Code will be available at https://github.com/294coder/RWKVFusion.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"115 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144693467","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}

引用次数: 0

M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising 基于多模态去噪的RGB-3D抗噪声工业异常检测

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-23 DOI: 10.1109/tpami.2025.3592089

Chengjie Wang, Haokun Zhu, Jinlong Peng, Yue Wang, Ran Yi, Yunsheng Wu, Lizhuang Ma, Jiangning Zhang

引用次数: 0

360VOTS: Visual Object Tracking and Segmentation in Omnidirectional Videos 360VOTS：全向视频中的视觉对象跟踪和分割

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-23 DOI: 10.1109/tpami.2025.3591725

Yinzhe Xu, Huajian Huang, Yingshu Chen, Sai-Kit Yeung

引用次数: 0

Multi-Channel Equilibrium Graph Neural Network for Multi-View Semi-Supervised Learning. 多视点半监督学习的多通道平衡图神经网络。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-21 DOI: 10.1109/tpami.2025.3587216

Shiping Wang,Yueyang Pi,Yang Huang,Fuhai Chen,Le Zhang

引用次数: 0

PointLLM-V2: Empowering Large Language Models to Better Understand Point Clouds PointLLM-V2：增强大型语言模型以更好地理解点云

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-21 DOI: 10.1109/tpami.2025.3590784

Runsen Xu, Shuai Yang, Xiaolong Wang, Tai Wang, Yilun Chen, Jiangmiao Pang, Dahua Lin

引用次数: 0

Monocular-to-3D Virtual Try-On with Generative Semantic Articulated Fields 单眼到三维虚拟试戴生成语义铰接场

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-21 DOI: 10.1109/tpami.2025.3591072

Zhenyu Xie, Fuwei Zhao, Jun Zheng, Xin Dong, Feida Zhu, Xiaodan Liang

引用次数: 0

Sel4FT: Annotation Selection for Pretraining-Finetuning With Distribution Shift 基于分布移位的预训练-微调的标注选择

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-21 DOI: 10.1109/tpami.2025.3591018

Han Lu, Yichen Xie, Mingyu Ding, Wei Zhan, Xiaokang Yang, Masayoshi Tomizuka, Junchi Yan

引用次数: 0

Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis 适用于图像分析的基于扩散的图像生成器

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-07-21 DOI: 10.1109/tpami.2025.3591076

Bingxin Ke, Kevin Qu, Tianfu Wang, Nando Metzger, Shengyu Huang, Bo Li, Anton Obukhov, Konrad Schindler

引用次数: 0