2025年计算机视觉与机器学习研究国际学术会议(CVML2025），由武汉大学和成都信息工程大学联合举办，将于2025年10月24-26日在四川省成都市举行。截稿日期2025年9月19日，诚邀广大师生投稿参会转发宣传！谢谢！
在CVML2025会议被录用且完成注册的论文，将由SPIE出版，并提交至EI核心以及Scopus检索。
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学术会议云：https://www.allconfs.org/meeting/index.asp?id=36418

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Conference information:
1. Conference Name: 2025 International Conference on Computer Vision and Machine Learning
2. Dates: October 24-26, 2025
3. Organizer: Wuhan University
4. Indexing: EI Compendex, Scopus
5. Format: Hybrid (Virtual & In-person)
6. Website: https://iccvml.com/

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INDEXING
Accepted and presented papers will be submitted to El Compendex and Scopus for indexing.

收录检索：EI Compendex，Scopus【多名大咖主讲 | EI稳定检索】

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FULL PAPER SUBMISSION
Please refer to the Submission Guidelines for specific information and submission requirements:
https://iccvml.com/?submissionguidelines/

请选择以下投稿方式之一：
CMT系统：https://cmt3.research.microsoft.com/CVML2025
电子邮件： iccvml@hotmail.com
注意：所有论文均应以英文撰写，篇幅不得少于4页。

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会议日期
一轮截稿日期：2025年9月19日
录用通知日期：投稿7个工作日内
会议召开日期：2025年10月24-26日
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Topic Areas

This is a non-comprehensive list of topics of interest to CVML 2025.  
1. Computer Vision and Imaging: 

- 3D from multi-view and sensors
- 3D from single images
- Autonomous driving
- Biometrics
- Computational imaging
- Computer vision theory
- Efficient and scalable vision 
- Explainable computer vision
- Humans: Face, body, pose, gesture, movement 
- Image and video synthesis and generation 
- Physics-based vision and shape-from-X
- Recognition: Categorization, detection, retrieval
- Scene analysis and understanding
- Segmentation, grouping, and shape analysis
- Video: Action and event understanding
	
2. Machine Leaning Techniques: 

- Adversarial attack and defense
- Deep learning architectures and techniques
- Machine learning (other than deep learning)
- Optimization methods (other than deep learning)
- Transfer/ low-shot/ continual/ long-tail learning
- Generative models
- Probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.) 
- Reinforcement learning 
- Representation learning for computer vision, audio, language,  and other modalities 
- Metric learning, kernel learning, and sparse coding
- Learning on graphs and other geometries and topologies

3. Ethics, Privacy, and Integrative Techniques:

- Transparency, fairness, accountability, privacy, and ethics in vision
- Vision, language, and reasoning
- Self-& semi-& meta-& unsupervised learning
- Robotics

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CONTACT

Dr. Deng: iccvml@hotmail.com
Ms. Li (Wechat): 17722135859