Conference Information

CVPR 2027: IEEE Conference on Computer Vision and Pattern Recognition

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Submission Date:
2026-11-10 Closed
Notification Date:
2027-02-25
Conference Date:
2027-06-20
Location:
Seattle, Washington, USA
Years:
CCF: A   ICORE: A*   QUALIS: A1   Viewed: 50781603   Tracked: 725   Attend: 123
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Conference Partner Index (CP-I)

94.9 / 100
Ranked #19 of 5,771 conferences · Top 1%

#1 of 247 in Computer Vision & Pattern Recognition

Academic recognition (35%)
100
Submission selectivity (20%)
85
Editions held (20%)
93
Community attention (10%)
92
Public record completeness (15%)
100

Inputs used: Listed as CCF A, QUALIS A1 · Acceptance rate: 24.1% (mean of 5 editions on file) · Editions on record: 31 · Researchers following it here: 725 · Researchers who opened this page in the past 24 months: 132

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.2 · Computed 2026-11-11

Researchers at these institutions track this conference

University of Chinese Academy of Sciences 28
Tsinghua University 27
Peking University 26
Beijing University of Posts and Telecommunications 20
Zhejiang University 20
Beihang University 18
University of Science and Technology of China 16
East China Normal University 15

Based on the institution researchers enter on their own profiles. We do not verify it.

Call For Papers

CVPR 2027 (IEEE Conference on Computer Vision and Pattern Recognition) is a CCF A / ICORE A* / QUALIS A1 conference held in Seattle, Washington, USA on 2027-06-20. The paper submission deadline is 2026-11-10. Acceptance notifications are sent on 2027-02-25.

Papers in the main technical program must describe high-quality, original research. Topics of interest cover all aspects of computer vision and pattern recognition including, but not limited to: 3D reconstruction and geometry: single-view, multi-view, depth, and sensors Trustworthy, responsible and explainable vision Generative AI Safety and Authenticity Deep learning architectures and techniques Computational imaging and cameras Vision applications, systems and social impact Theory, optimization, and statistical learning for vision Low-level vision Physics-based vision and shape-from-X Document analysis and understanding Human-centric vision and biometrics Data synthesis, datasets and benchmarks Efficient and scalable vision Multimodal learning Image and video synthesis and generation Mulitmodal Generative and Foundation Models Reasoning & Reinforcement Learning Recognition: Categorization, detection, retrieval Segmentation, grouping and shape analysis Representation learning Embodied vision and robotics Agentic AI Physical AI: World Models, VLA, Planning, Simulation Autonomous driving Scene analysis and understanding Self-, semi-, meta- and unsupervised learning Transfer / low-shot / continual / long-tail learning Photogrammetry, remote sensing and multi-sensors Video understanding, motion and tracking Vision + graphics AI for Science and Scientific Discovery AI for Education Medical and biological vision, cell microscopy
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Acceptance Ratio

Average acceptance rate: 17.2% over 23 years (1997–2025).

YearSubmittedAcceptedAccepted(%)
202513008287822.1%
202411532271923.6%
20239155236025.8%
20228161206725.3%
20217015166123.7%
20206656147022.1%
20195160129425.1%
2018330397929.6%
2017262078329.9%
2016214564330%
2015212360228.4%
2014180754029.9%
20091464614.2%
20081593634%
20071250604.8%
20061131544.8%
20051160746.4%
2004873546.2%
2003905606.6%
2001920788.5%
20004666614.2%
1998453429.3%
19975446211.4%

Best Papers

YearBest Papers
2026Efficiently Reconstructing Dynamic Scenes One D4RT at a Time
2025Neural Inverse Rendering from Propagating Light
2025VGGT: Visual Geometry Grounded Transformer
2024Rich Human Feedback for Text-to-Image Generation
2024Generative Image Dynamics
2023Planning-oriented Autonomous Driving
2023Visual Programming: Compositional visual reasoning without training
2022Learning to Solve Hard Minimal Problems
2021GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields
2020Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
2019A Theory of Fermat Paths for Non-Line-of-Sight Shape Reconstruction
2018Taskonomy: Disentangling Task Transfer Learning
2017Densely Connected Convolutional Networks
2017Learning from Simulated and Unsupervised Images through Adversarial Training
2016Deep Residual Learning for Image Recognition
2015DynamicFusion: Reconstruction and Tracking of Non-rigid Scenes in Real-Time
2014What Camera Motion Reveals About Shape with Unknown BRDF
2013Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
2012A Simple Prior-free Method for Non-Rigid Structure-from-Motion Factorization
2011Real-time human pose recognition in parts from single depth images
2010Efficient computation of robust low-rank matrix approximations in the presence of missing data using the L1 norm
2009Single image haze removal using dark channel prior
2008Beyond sliding windows: Object localization by efficient subwindow search
2008Global stereo reconstruction under second order smoothness priors
2007Dynamic 3D Scene Analysis from a Moving Vehicle
2006Putting Objects in Perspective
2005Real-Time Non-Rigid Surface Detection
2004Programmable Imaging using a Digital Micromirror Array
2003Object Class Recognition by Unsupervised Scale-Invariant Learning
2001Morphable 3D models from video
2000A New Algorithm for Non-Rigid Point Matching
1999Robust Hierarchical Algorithm for Constructing a Mosaic from Images of the Curved Human Retina
1998Optimal Structure from Motion: Local Ambiguities and Global Estimates
1997What is a Light Source?
1997Learning Bilinear Models for Two-Factor Problems in Vision
1996What is the Set of Images of an Object Under All Possible Lighting Conditions?

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