会议信息

WACV 2027: IEEE/CVF Winter Conference on Applications of Computer Vision

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截稿日期:
2026-08-28 已截稿
通知日期:
2026-10-09
会议日期:
2027-01-04
会议地点:
Buena Vista, Florida, USA
届数:
ICORE: A   QUALIS: B1   浏览: 46134   关注: 32   参加: 2
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会伴指数 (CP-I)

58.5 / 100
全站第 663 名 / 共 5,759 个会议 · 前 12%

计算机视觉与模式识别 第 24 / 246

学术认可 (35%)
88
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
19
社区关注 (10%)
58
资料公开度 (15%)
55

用到的输入: 收录等级：ICORE A, QUALIS B1 · 有据可查的届次：1 · 在会伴关注它的研究者：32 人 · 过去 24 个月打开过本页的研究者：23 人

公开资料里还缺: 历年录用率 (+4.5) · 最佳论文记录 (+2.3)
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 80% —— 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 2026-10-24

征稿

WACV 2027 (IEEE/CVF Winter Conference on Applications of Computer Vision) is a ICORE A / QUALIS B1 conference held in Buena Vista, Florida, USA on 2027-01-04. The paper submission deadline is 2026-08-28. Acceptance notifications are sent on 2026-10-09.

IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) provides a forum for computer vision researchers working on practical applications and innovative algorithms to share their latest developments. Applications Track WACV 2027 solicits high-quality, original submissions describing research in computer vision, with a particular emphasis on systems and applications with significant, interesting vision components. Application areas include, but are not limited to: Agriculture Animals and Insects Arts, games, and social media Autonomous driving Biomedical, healthcare, and medicine Commercial and retail Education Embedded sensing and real-time techniques Environmental monitoring, climate change, and ecology Food science and nutrition Psychology and cognitive science Remote sensing Robotics Smartphones and end-user devices Social good Structural engineering and civil engineering Virtual and augmented reality Visualization Algorithms Track Authors are also encouraged to submit more traditional computer vision algorithms papers. Topics of interest include, but are not limited to: 3D computer vision Adversarial learning, adversarial attack, and defense methods Biometrics, face, gesture, and body pose Computational photography Generative models for image, video, 3D, etc. Explainable, fair, accountable, privacy-preserving, and ethical computer vision Image recognition and understanding (object detection, categorization, segmentation, scene modeling, visual reasoning) Low-level and physics-based vision Machine learning architectures, formulations, and algorithms (including transfer, low-shot, semi-, self-, and unsupervised learning) Video recognition and understanding (tracking, action recognition, etc.) Vision + language and/or other modalities Foundation models for downstream vision tasks Evaluations & Datasets Track Authors are also encouraged to submit papers to the Evaluations & Datasets (E&D) Track. These submissions must advance the science and practice of evaluation in computer vision including the development and use of datasets and other resources. Topics of interest include, but are not limited to: Analysis of strengths, limitations, or failure modes of existing benchmarks or evaluation practices Study benchmark saturation or overfitting and their impact on scientific conclusions Compare evaluation designs and demonstrate how different assumptions lead to different results or conclusions Provide rigorous reproduction, auditing, and stress-testing of prior evaluations Propose new evaluation protocols, practices, or methodologies Conduct human- or interaction-centered evaluations (e.g., user studies, red-teaming) Introduce datasets and clearly explain their scope, assumptions, limitations, and how they are intended to support or shape evaluative claims across the computer vision lifecycle Contribute tools, analyses, or frameworks that improve how evaluative claims are constructed or interpreted Present negative results, critical analyses, and use-case-inspired evaluations New datasets and dataset collections Audits of existing datasets Benchmarks on new or existing datasets, benchmarking tools, and methodologies Systematic analyses of systems on novel datasets
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