Conference Information

CVML 2026: International Conference on Computer Vision and Machine Learning

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Submission Date:
2026-09-11
Notification Date:
Conference Date:
2026-09-18
Location:
Nanchang, China
Years:
Viewed: 9054   Tracked: 1   Attend: 0

Conference Partner Index (CP-I)

44.3 / 100
Ranked #3,091 of 5,682 conferences · Top 55%

#160 of 244 in Computer Vision & Pattern Recognition #360 of 739 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
30
Community attention (10%)
26
Public record completeness (15%)
55

Inputs used: Editions on record: 2 · Researchers following it here: 1 · Researchers who opened this page in the past 24 months: 12

Missing from the public record: Historical acceptance rates (+4.5) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-15

Call For Papers

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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