会议信息

ECCV 2026: European Conference on Computer Vision

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截稿日期:
2026-03-06 已截稿
通知日期:
2026-05-09
会议日期:
2026-09-08
会议地点:
Malmo, Sweden
届数:
18
CCF: B   ICORE: A*   QUALIS: A1   浏览: 1160390   关注: 383   参加: 55
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ECCV
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会伴指数 (CP-I)

89.1 / 100
全站第 90 名 / 共 5,763 个会议 · 前 2%

计算机视觉与模式识别 第 3 / 246

学术认可 (35%)
100
投稿选择性 (20%)
81
会议传承 (20%)
90
社区关注 (10%)
79
资料公开度 (15%)
80

用到的输入: 收录等级：CCF B, QUALIS A1 · 录用率：27.7%（有记录的 5 届的均值） · 有据可查的届次：27 · 在会伴关注它的研究者：383 人 · 过去 24 个月打开过本页的研究者：24 人

公开资料里还缺: 历届信息 (+3.0)
主办方认领本会议后，可直接在这一页补上；分数每晚重算。如何提升这个分数

置信度 100% —— 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 1.2 · 算于 2026-10-30

征稿

ECCV 2026 (European Conference on Computer Vision) is a CCF B / ICORE A* / QUALIS A1 conference held in Malmo, Sweden on 2026-09-08. The paper submission deadline is 2026-03-06. Acceptance notifications are sent on 2026-05-09.

Topics Papers in the main technical program must describe high-quality, original research. Topics of interest include all aspects of computer vision and pattern recognition, including, but not limited to: 3D from multi-view and sensors 3D from single images Adversarial attack and defense Autonomous driving Biometrics Camera pose estimation and calibration Computational imaging Computer vision for robotics Computer vision for social good Deep learning architectures and techniques Document analysis and understanding Efficient and scalable vision Embodied vision: active agents, simulation Event-based cameras Explainable computer vision First person (egocentric) vision Geometric deep learning Humans: face, body, pose, gesture, movement Image and video synthesis and generation Low-level vision Machine learning (other than deep learning) Medical and biological vision, cell microscopy Multimodal learning (other than language) (Neural) scene representations Object pose estimation and tracking Optimization methods (other than deep learning) Photogrammetry and remote sensing Physics-based vision and shape-from-X Recognition: categorization, detection, retrieval Representation learning Scene analysis and understanding Segmentation, grouping and shape analysis Self-, semi-, meta- and unsupervised learning Synthetic data and simulation Transfer, low-shot, continual, long-tail learning Transparency, fairness, accountability, and ethics in vision Video: action and event understanding Video: low-level analysis, motion, and tracking Vision + graphics Vision for edge devices Vision, language, and reasoning Vision for climate and sustainability Visual localization, SLAM, Structure-from-Motion Safety and privacy
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录用率

平均录用率: 22% 10 年间 (1998–2024).

年份提交数录用数录用率(%)
20248584239527.9%
20205025136127.1%
2018243977631.8%
2016156141526.6%
2014144436325.1%
2006900404.4%
2004555417.4%
2002600457.5%
200026611643.6%
19982234218.8%

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CCF全称影响因子出版商ISSN
BSoftware & Systems Modeling3.2Springer1619-1366
BPattern Recognition7.6Elsevier0031-3203
BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
BInformation Sciences6.0Elsevier0020-0255
BComputer Networks4.7Elsevier1389-1286
BComputers & Security5.4Elsevier0167-4048
BIEEE Transactions on Intelligent Transportation Systems8.4IEEE1524-9050
BJournal of Systems and Software3.8Elsevier0164-1212
BInformation Processing & Management6.9Elsevier0306-4573
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