TPAMI-2025 综述文章

Toward Few-Shot Learning in the Open World: A Review and Beyond

文章解读: 开放世界中的小样本学习：综述与展望
http://www.studyai.com/xueshu/paper/detail/1279308036

文章链接:(10.1109/TPAMI.2025.3594686)

Diffusion Models in Low-Level Vision: A Survey

文章解读: 低级视觉中的扩散模型：一项调查
http://www.studyai.com/xueshu/paper/detail/1876609176

文章链接:(10.1109/TPAMI.2025.3545047)

Foundation Models Defining a New Era in Vision: A Survey and Outlook

文章解读: 基础模型定义视觉新纪元：一项调查与展望
http://www.studyai.com/xueshu/paper/detail/3289573020

文章链接:(10.1109/TPAMI.2024.3506283)

Efficient Diffusion Models: A Comprehensive Survey From Principles to Practices

文章解读: 高效扩散模型：从原理到实践的全面综述
http://www.studyai.com/xueshu/paper/detail/3623250692

文章链接:(10.1109/TPAMI.2025.3569700)

Diffusion Model-Based Image Editing: A Survey

文章解读: 基于扩散模型的图像编辑：一项调查
http://www.studyai.com/xueshu/paper/detail/5376630776

文章链接:(10.1109/TPAMI.2025.3541625)

Few-Shot Class-Incremental Learning for Classification and Object Detection: A Survey

文章解读: 小样本增量学习在分类和目标检测中的应用：一项调查
http://www.studyai.com/xueshu/paper/detail/6096163853

文章链接:(10.1109/TPAMI.2025.3529038)

Self-Supervised Multimodal Learning: A Survey

文章解读: 自监督多模态学习：一项调查
http://www.studyai.com/xueshu/paper/detail/6197312292

文章链接:(10.1109/TPAMI.2024.3429301)

Human Motion Video Generation: A Survey

文章解读: 人体运动视频生成：一项综述
http://www.studyai.com/xueshu/paper/detail/6735281851

文章链接:(10.1109/TPAMI.2025.3594034)

Graph Anomaly Detection in Time Series: A Survey

文章解读: 时间序列中的图异常检测：一项调查
http://www.studyai.com/xueshu/paper/detail/6911597710

文章链接:(10.1109/TPAMI.2025.3566620)

RenAIssance: A Survey Into AI Text-to-Image Generation in the Era of Large Model

文章解读: RenAIssance：大型模型时代人工智能文本到图像生成技术综述
http://www.studyai.com/xueshu/paper/detail/7185571752

文章链接:(10.1109/TPAMI.2024.3522305)

Towards Data-And Knowledge-Driven AI: A Survey on Neuro-Symbolic Computing

文章解读: 迈向数据与知识驱动的AI：神经符号计算综述
http://www.studyai.com/xueshu/paper/detail/7677390876

文章链接:(10.1109/TPAMI.2024.3483273)

Out-of-Distribution Generalization on Graphs: A Survey

文章解读: 图上的分布外泛化：一项调查
http://www.studyai.com/xueshu/paper/detail/8551638182

文章链接:(10.1109/TPAMI.2025.3593897)

A Survey and Benchmark of Automatic Surface Reconstruction From Point Clouds

文章解读: 从点云中进行自动表面重建的综述与基准测试
http://www.studyai.com/xueshu/paper/detail/8710057569

文章链接:(10.1109/TPAMI.2024.3510932)

When Meta-Learning Meets Online and Continual Learning: A Survey

文章解读: 当元学习遇见在线和持续学习：综述
http://www.studyai.com/xueshu/paper/detail/8918607627

文章链接:(10.1109/TPAMI.2024.3463709)

Deep Learning-Based Point Cloud Compression: An In-Depth Survey and Benchmark

文章解读: 基于深度学习的点云压缩：深入综述与基准测试
http://www.studyai.com/xueshu/paper/detail/8935003120

文章链接:(10.1109/TPAMI.2025.3594355)

Towards Unified Deep Image Deraining: A Survey and a New Benchmark

文章解读: 面向统一的深度图像去雨：一项调查和一个新的基准
http://www.studyai.com/xueshu/paper/detail/8956095322

文章链接:(10.1109/TPAMI.2025.3556133)

The Synergy Between Data and Multi-Modal Large Language Models: A Survey From Co-Development Perspective

文章解读: 数据和多模态大语言模型的协同作用：从协同开发视角的综述
http://www.studyai.com/xueshu/paper/detail/9766383980

文章链接:(10.1109/TPAMI.2025.3576835)

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning

文章解读: 超越持续学习，深度学习中的遗忘综合研究
http://www.studyai.com/xueshu/paper/detail/9771055056

文章链接:(10.1109/TPAMI.2024.3498346)

Event-Based Stereo Depth Estimation: A Survey

文章解读: 基于事件的立体深度估计：一项调查
http://www.studyai.com/xueshu/paper/detail/9889827315

文章链接:(10.1109/TPAMI.2025.3586559)