Submission Instructions

We welcome full paper submissions. The papers must be no longer than 8 pages in total (excluding references). Please submit at the following CMT website:
ADFM 2026 CMT submission website.

• All submissions are handled via the workshop’s CMT website.
• Submissions should be made in PDF format and should follow the official CVPR 2026 template and guidelines.
• All submissions must be anonymous and conform to the CVPR 2026 conference guidelines for double-blind review.
• ADFM 2026 workshop follows the CVPR 2026 conference guidelines and does not allow dual submissions.
• Authors may upload optional supplementary materials, containing additional details, videos, images, etc. in a separate zip file (with a max of 50MB in size); the deadline for submitting these supplementary materials is the same as that for the main paper.
• The accepted papers will be presented as either an oral, spotlight, or poster presentation.
• By submitting a paper to the ADFM 2026 workshop for review, the authors must agree that at least one author or authors' representative of each accepted submission must present the paper at the workshop in-person and that they are willing and able to serve as the reviewers of the ADFM 2026 workshop submissions if needed (decided by the ADFM 2026 workshop organizing team).
• The presentation of accepted papers at our workshop will follow the same policy as that for the accepted papers at the CVPR 2026 main conference.
• The accepted papers will be made publicly accessible on the workshop website shortly after the camera-ready deadline. CVPR 2026 will provide the official proceedings of the accepted papers.
• Failure to comply with the aforesaid rules may cause the paper to be removed from the workshop program.

Submission deadline:       February 27 March 3, 2026 11:59 PM EDT (extended)  
Notification to authors:    March 20, 2026
Camera ready deadline:   April 10, 2026

We invite the submission of original and high-quality research papers in the topics related to anomaly detection with foundation models.

We're seeking dedicated Reviewers! Please self-nominate via the reviewer self-nomination form. Thanks for your support!

Topics

The topics for ADFM 2026 include, but are not limited to:

• Fundamental theories and principles of foundation models for anomaly detection.
• Advanced anomaly detection algorithms and frameworks utilizing foundation models.
• Sector-specific anomaly detection employing foundation models, covering areas such as finance, healthcare, cybersecurity, and industrial systems.
• Evaluation standards and benchmarks for appraising anomaly detection in foundation models.
• Methods enhancing the clarity and comprehensibility of foundation models in anomaly detection.
• Methods promoting fairness and diminishing bias in anomaly detection with foundation models.
• Privacy-enhancing techniques in anomaly detection with foundation models.
• Trust and reliability of foundation models in crucial anomaly detection applications.
• Interdisciplinary methods for refining anomaly detection, incorporating insights from fields like psychology and sociology.
• Adaptive learning and adjustment mechanisms for foundation models in dynamic settings.
• Integration of expert knowledge and domain-specific systems with foundation models for enhanced anomaly detection.
• Exploratory discussions on the constraints and challenges of current foundation models in identifying anomalies in complex and noisy datasets.
• Prospective insights on the evolution of anomaly detection methods with the advancement of foundation models.

 

Accepted Papers

• [200] SCL: Towards Domain Generalization via Single-Temporal Multimodal Contrastive Learning for Remote Sensing Change Detection.
  Du, Qiangang; Peng, Jinlong; Chen, Xu; He, Qingdong; He, Liren; Nie, Qiang; Chi, Mingmin.


• [201] SAM-OOD: Foundation-Model-Guided Unknown Mining for Object-Level Anomaly Detection.
  Lee, Seung-Ik; Kanwal, Seher.

Akash Sahoo	Texas A&M University
Bo Chen	Middle Tennessee State University
Cheng Fei	Cornell University
Chloe Qinyu Zhu	Duke University
Chunyu N. Yuan	CUNY
Dan Le	Google DeepMind
Duran Bao	Tulane University
Feng Xu	UNC Chapel Hill
Gaoyuan Du	University of Tennessee, Knoxville
Hanwen Liu	University of Southern California
Hehuan Liu	Tencent
Jigar Surana	Meta
Kun Xu	Nanjing University of Aeronautics and Astronautics
Manyi Yao	University of California, Riverside
Md Sultanul Islam Ovi	George Mason University
Myles Joshua Tan	University of Florida
Nam Nguyen	Oregon State University
Poojitha Thota	The University of Texas at Arlington
Qi Song	Hong Kong Baptist University
Ren Wang	Seoul National University
Tai Vu	OpenAI
Taibiao Zhao	Louisiana State University
Tooba Khan	University of Southern California
Vineet Punyamoorty	Purdue University
Weihang Xiao	Amazon
Weiheng Bai	University of Minnesota
Yanlin Chen	Trine University
Yining Zhou	Texas A&M University
Yinuo Wang	Trine University
Yu Sun	Meta
Yueqi Tian	University of Rochester
Yuhan Wei	Rice University
Yuning Yang	U.S. FDA
Yuqing Hou	TsingHua University
Yuqing Wang	Microsoft
Yuxi Tan	University of California, Santa Barbara