Información de la conferencia

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

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Día de Entrega:
2026-08-28 Cerrado
Fecha de Notificación:
2026-10-09
Fecha de conferencia:
2027-01-04
Ubicación:
Buena Vista, Florida, USA
Ediciones:
ICORE: A   QUALIS: B1   Vistas: 46121   Seguidores: 32   Asistentes: 2
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Índice Conference Partner (CP-I)

58,5 / 100
Puesto n.º 663 de 5.759 congresos · 12% superior

N.º 24 de 246 en Visión por computador y reconocimiento de patrones

Reconocimiento académico (35%)
88
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
19
Atención de la comunidad (10%)
58
Integridad del registro público (15%)
55

Datos utilizados: Categorías: ICORE A, QUALIS B1 · Ediciones documentadas: 1 · Investigadores que lo siguen aquí: 32 · Investigadores que abrieron esta página en los últimos 24 meses: 23

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Premios al mejor artículo (+2,3)
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Confianza 80 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.2 · Calculado el 2026-10-24

Solicitud de Artículos

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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