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CVML 2026: International Conference on Computer Vision and Machine Learning

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截稿日期:
2026-09-11
通知日期:
会议日期:
2026-09-18
会议地点:
Nanchang, China
届数:
浏览: 9056   关注: 1   参加: 0

会伴指数 (CP-I)

44.3 / 100
全站第 3,091 名 / 共 5,682 个会议 · 前 55%

计算机视觉与模式识别 第 160 / 244 人工智能与机器学习 第 360 / 739

学术认可 (35%) 无数据 —— 按中性基准 50 分计入 —
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
30
社区关注 (10%)
26
资料公开度 (15%)
55

用到的输入: 有据可查的届次：2 · 在会伴关注它的研究者：1 人 · 过去 24 个月打开过本页的研究者：12 人

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置信度 45% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-15

征稿

CVML 2026 (International Conference on Computer Vision and Machine Learning) is an academic conference held in Nanchang, China on 2026-09-18. The paper submission deadline is 2026-09-11.

Topic Areas This is a non-comprehensive list of topics of interest to CVML 2026. 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 - Biomedical imaging and data analysis - Computational imaging and multi-modal sensing - 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 - Representation learning for vision, audio, signals, and biomedical modalities 3. Ethics, Privacy, and Integrative Techniques: - Transparency, fairness, accountability, privacy, and ethics in vision - Privacy, security, and ethics in cross-domain ML/CV applications - Vision, language, and reasoning - Self-& semi-& meta-& unsupervised learning - Robotics
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