Full Papers
High-quality, original research papers submitted to the main technical program, with a submission deadline of November 13, 2025 AOE and supplementary materials due by November 20, 2025 AOE.
AI/CORE A*
CVPR 2026
IEEE/CVF Conference on Computer Vision and Pattern Recognition
CVPR 2026 is the IEEE/CVF Conference on Computer Vision and Pattern Recognition, featuring a main technical program for high-quality, original research in all aspects of computer vision and pattern recognition. The conference includes workshops, tutorials, demos, and other activities, with all main conference papers publicly released two weeks before the event and a strong emphasis on ethical considerations, community building, and interdisciplinary topics.
Key deadlines
Verified September 9, 2026Abstract registration
November 7, 2025
AoE
Full paper
November 14, 2025
AoE
Conference timeline
Submission and decisions
Abstract registrationKey deadline
November 7, 2025 · AoE
Full paperKey deadline
November 14, 2025 · AoE
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Workshop Proposals
Proposals for workshops to be held on June 3–4, 2026, submitted via OpenReview, limited to 8 pages, with a deadline of November 3, 2025 AOE.
Tutorial Proposals
Proposals for short courses and tutorials to be held on June 3–4, 2026, submitted via Google Form, with a deadline of December 15, 2025 AOE.
Demos
Demonstrations of systems or applications, with a submission deadline of March 20, 2026 AOE.
Broadening Participation Submissions
Submissions to support diversity and inclusion efforts, with a deadline of March 31, 2026 AOE.
Doctoral Consortium Participation
Applications for doctoral students to present and receive feedback on their research, with a call for participation issued on March 17, 2026.
Musical Performance Submissions
Submissions for musical performances associated with the conference, as referenced in the guides section.
Research areas in scope
01
Main Conference Topics
3D from multi-view and sensors3D from single imagesAdversarial attack and defenseAutonomous drivingBiometricsComputational imagingComputer vision for social goodComputer vision theoryDatasets and evaluationDeep learning architectures and techniquesDocument analysis and understandingEfficient and scalable visionEmbodied vision: Active agents, simulationEvent-based camerasExplainable computer visionHumans: Face, body, pose, gesture, movementImage and video synthesis and generationLow-level visionMachine learning (other than deep learning)Medical and biological vision, cell microscopyMultimodal learningOptimization methods (other than deep learning)Photogrammetry and remote sensingPhysics-based vision and shape-from-XRecognition: Categorization, detection, retrievalRepresentation learningComputer Vision for RoboticsScene analysis and understandingSegmentation, grouping and shape analysisSelf-, semi-, meta- and unsupervised learningTransfer/ low-shot/ continual/ long-tail learningTransparency, fairness, accountability, privacy and ethics in visionVideo: Action and event understandingVideo: Low-level analysis, motion, and trackingVision + graphicsVision, language, and reasoningVision applications and systems
02
Workshop Tracks
3D from multi-view and sensors3D from single imagesActive learningAdversarial attack and defenseAffinity groupsAutonomous drivingBiometricsComputational imagingComputer vision theoryDeep learning architectures and techniquesDocument analysis and understandingEfficient, edge, and scalable visionEgocentric visionEmbodied vision: Active agents, simulationEvent-based camerasExplainable computer visionFoundation models (LLM, VLM, VLA, etc.)Generative modelsHuman modeling & understanding: Face, body, pose, gesture, movementImage and video synthesis and generationLow-level visionMachine learning (other than deep learning)Medical and biological vision, cell microscopyMultimodal detection, recognition, segmentationMultimodal learningReinforcement learning and reasoningOpen world learningOptimization methods (other than deep learning)Photogrammetry and remote sensingPhysics-based vision and shape-from-XRecognition: categorization, detection, retrievalRepresentation learningRobot perceptionScene analysis and understandingSegmentation, grouping and shape analysisSelf-, semi-, meta- and unsupervised learningSynthetic data for visionTransfer/ low-shot/ continual/ long-tail learningTransparency, safety, fairness, accountability, and ethics in visionVideo: Action and event understandingVideo: Low-level analysis, motion, and trackingVision and graphicsVision for accessibilityVision for healthcareVision for privacy and securityVision for scientific discoveryVision for societal goodVision for XR, AR, VRVision, language, and reasoningVision applications and systemsWorld modelsEmerging topics - other
Policies worth checking twice
- Dual Submission Policy: No substantially similar paper may be submitted to another conference or workshop during the review period (November 13, 2025 – February 20, 2026).
- Mandatory Reviewer Service: All authors must commit to serve as reviewers, area chairs, or senior area chairs when invited, with exceptions granted only for exceptional circumstances approved in advance.
- OpenReview Profile Requirement: All co-authors must have an up-to-date OpenReview profile to submit a paper; new profiles without institutional email require up to two weeks for moderation.
- Publication and Patent Disclosure: Accepted papers are publicly released by the Computer Vision Foundation two weeks before the conference; authors must consider this as the official public disclosure date for patent purposes.
- Ethical Use of Information: Any use, exploitation, or sharing of information obtained through the November 2025 OpenReview security incident is strictly prohibited and results in immediate desk rejection and multi-year bans.
- Workshop Paper Publication: Workshops may publish papers in CVPR proceedings, but must adhere to CVPR 2026 paper format, length, and camera-ready deadlines (tentatively April 11, 2026).
- Dataset Citation Policy: Workshop challenges must not require participants to cite more than one or two papers to use a dataset; excessive citation requirements may lead to workshop removal.
- No Fully Virtual Workshops: All workshops must be in-person or hybrid; fully virtual workshops are not permitted.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Official conference siteCall for PapersCall for WorkshopsCall for TutorialsAuthor GuidelinesAuthor Compute Reporting Form
Last verified September 9, 2026