IEEE Transactions on Pattern Analysis and Machine Intelligence最新文献

Explicit Visual Prompting for Universal Foreground Segmentations 通用前景分割的显式视觉提示

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-09 DOI: 10.1109/tpami.2025.3619490

Weihuang Liu, Xi Shen, Chi-Man Pun, Xiaodong Cun

引用次数: 0

Unified Cross-Modal Medical Image Synthesis with Hierarchical Mixture of Product-of-Experts 基于层次混合专家产品的统一跨模态医学图像合成

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-08 DOI: 10.1109/tpami.2025.3616632

Reuben Dorent, Nazim Haouchine, Alexandra Golby, Sarah Frisken, Tina Kapur, William Wells

引用次数: 0

Layer-Adaptive-Augmentation-Based Graph Contrastive Learning With Feature Decorrelation. 基于层自适应增强的特征去相关图对比学习。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-07 DOI: 10.1109/tpami.2025.3618329

Yuhua Xu,Junli Wang,Rui Duan,Changjun Jiang

{"title":"Layer-Adaptive-Augmentation-Based Graph Contrastive Learning With Feature Decorrelation.","authors":"Yuhua Xu,Junli Wang,Rui Duan,Changjun Jiang","doi":"10.1109/tpami.2025.3618329","DOIUrl":"https://doi.org/10.1109/tpami.2025.3618329","url":null,"abstract":"Graph Contrastive Learning (GCL) methods typically leverage augmentation techniques to generate different graph views for comparison, thereby learning corresponding representations for graph-related tasks in label-scarce scenarios. However, existing GCL methods suffer from two primary limitations: 1) they use predefined or one-time perturbations for augmentation, ignoring adaptive noise injection during forward propagation and thus leading to suboptimal model robustness; 2) their contrast mechanisms mainly focus on the agreement of inter-graph representations while neglecting the dimensional feature redundancy within intra-graph representations. To solve these issues, we propose Layer-adaptive-augmentation-based Graph Contrastive Learning with feature Decorrelation (LGCLD). Firstly, the designed layer- wise adaptive augmentation method performs dynamic perturbations while maintaining the semantic similarity between augmented and original graphs, which can improve model robustness. Secondly, we introduce an Agreement-Decorrelation loss (AD loss) that simultaneously optimizes the agreement between graph-level representations and the feature correlation among different dimensions within each graph-level representation, promoting the model to learn informative and non-redundant graph-level representations. Furthermore, we analyze the reasonableness of AD loss through the graph information bottleneck principle. Experiments on various-domain graph datasets demonstrate that LGCLD achieves better or competitive performance compared with a series of state-of-the-art baselines.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"31 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240868","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

Wonder3D++: Cross-Domain Diffusion for High-Fidelity 3D Generation From a Single Image. wonder3d++：用于高保真3D生成的跨域扩散从单个图像。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-07 DOI: 10.1109/tpami.2025.3618675

Yuxiao Yang,Xiaoxiao Long,Zhiyang Dou,Cheng Lin,Yuan Liu,Qingsong Yan,Yuexin Ma,Haoqian Wang,Zhiqiang Wu,Wei Yin

{"title":"Wonder3D++: Cross-Domain Diffusion for High-Fidelity 3D Generation From a Single Image.","authors":"Yuxiao Yang,Xiaoxiao Long,Zhiyang Dou,Cheng Lin,Yuan Liu,Qingsong Yan,Yuexin Ma,Haoqian Wang,Zhiqiang Wu,Wei Yin","doi":"10.1109/tpami.2025.3618675","DOIUrl":"https://doi.org/10.1109/tpami.2025.3618675","url":null,"abstract":"In this work, we introduce Wonder3D++, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. To holistically improve the quality, consistency, and efficiency of single-view reconstruction tasks, we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. To ensure the consistency of generation, we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a cascaded 3D mesh extraction algorithm that drives high-quality surfaces from the multi-view 2D representations in only about 3 minute in a coarse-to-fine manner. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and good efficiency compared to prior works.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"56 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240870","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

Probabilistically Aligned View-unaligned Clustering with Adaptive Template Selection 具有自适应模板选择的概率对齐视图不对齐聚类

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-07 DOI: 10.1109/tpami.2025.3618984

Wenhua Dong, Xiao-Jun Wu, Zhenhua Feng, Sara Atito, Muhammad Awais, Josef Kittler

引用次数: 0

Deep Neural Network Parameter Selection via Dataset Similarity under Meta-Learning Framework. 元学习框架下基于数据集相似度的深度神经网络参数选择。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-07 DOI: 10.1109/tpami.2025.3618991

Liping Deng,Maziar Raissi,MingQing Xiao

{"title":"Deep Neural Network Parameter Selection via Dataset Similarity under Meta-Learning Framework.","authors":"Liping Deng,Maziar Raissi,MingQing Xiao","doi":"10.1109/tpami.2025.3618991","DOIUrl":"https://doi.org/10.1109/tpami.2025.3618991","url":null,"abstract":"Optimizing the performance of deep neural networks (DNNs) remains a significant challenge due to the sensitivity of models to both hyperparameter selection and weight initialization. Existing approaches typically address these two factors independently, which often leads to limiting adaptability and overall effectiveness. In this paper, we present a novel meta-learning framework that jointly recommends hyperparameters and initial weights by leveraging dataset similarity. Our method begins by extracting meta-features from a collection of historical datasets. For a given query dataset, similarity is computed based on distances in the meta-feature space, and the most similar historical datasets are used to recommend the underlying parameter configurations. To capture the diverse characteristics of image datasets, we introduce two complementary types of meta-features. The first, referred to as shallow or visible meta-features, comprises five groups of statistical measures that summarize color and texture information. The second, termed deep or invisible meta-features, consists of 512 descriptors extracted from a convolutional neural network pre-trained on ImageNet. We evaluated our framework in 105 real-world image classification tasks, using 75 datasets for historical modeling and 30 for querying. Experimental results with both vision transformers and convolutional neural networks demonstrate that our approach consistently outperforms state-of-the-art baselines, underscoring the effectiveness of dataset-driven parameter recommendation in deep learning.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"58 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145240879","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

SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation SMPLest-X：表达人体姿势和形状估计的终极缩放

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-07 DOI: 10.1109/tpami.2025.3618174

Wanqi Yin, Zhongang Cai, Ruisi Wang, Ailing Zeng, Chen Wei, Qingping Sun, Haiyi Mei, Yanjun Wang, Hui En Pang, Mingyuan Zhang, Lei Zhang, Chen Change Loy, Atsushi Yamashita, Lei Yang, Ziwei Liu

引用次数: 0

Model-free Test Time Adaptation for out-Of-Distribution Detection 非分布检测的无模型测试时间自适应

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-06 DOI: 10.1109/tpami.2025.3615192

YiFan Zhang, Xue Wang, Tian Zhou, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin

引用次数: 0

Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detection 学习基于知识的提示鲁棒3D掩码表示攻击检测

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-06 DOI: 10.1109/tpami.2025.3618630

Fangling Jiang, Qi Li, Bing Liu, Weining Wang, Caifeng Shan, Zhenan Sun, Ming-Hsuan Yang

引用次数: 0

PiercingEye: Dual-Space Video Violence Detection With Hyperbolic Vision-Language Guidance 穿孔眼：双曲视觉语言引导下的双空间视频暴力检测

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-06 DOI: 10.1109/tpami.2025.3617460

Jiaxu Leng, Zhanjie Wu, Mingpi Tan, Mengjingcheng Mo, Jiankang Zheng, Qingqing Li, Ji Gan, Xinbo Gao

引用次数: 0