Overview

Anomaly detection, and the synonymous topics of novelty and out-of-distribution detection, represent an important and application-relevant challenge within both computer vision and the broader field of pattern recognition. In its simplest formulation, anomaly detection targets the identification of samples which deviate from an obtained approximation to the true distribution of normality for a given dataset. As such anomalies represent unexpected eventualities or outliers in the scope of a given task. The notion of detecting them effectively and efficiently has been sought after for many real-world applications including medical diagnosis, airport security screening, industrial inspection, or crowd control.

However, anomaly detection is far from a simple task due to the challenges of accounting for all forms with which an anomaly may be present. It is typically impossible for any given dataset to account for the complete anomalous variability as they represent an unbounded (open set) distribution of possible deviations from the distribution of normality. Established supervised techniques are therefore prone to suffer from heavy classification bias or over-fitting.

To these ends, we now see the rise of a complex and vibrant set of learning-based paradigms addressing the anomaly detection task – varying across both the fully/semi/un-supervised and few/one/zero shot axes of recent computer vision and pattern recognition research. This workshop brings together researchers of both industry and academia to present and discuss recent developments, opportunities and open challenges in this area. The workshop will also host an anomaly detection challenge, to encourage the development and benchmarking new algorithms for realistic yet challenging tasks.

Challenges

Please check the Challenge page for more details.

Call for Papers

Our call for papers includes the following topics:

Anomaly detection, novelty detection, and out-of-distribution detection in images and videos.
Relevant learning paradigms including unsupervised, few-shot and active learning.
Dataset challenges including highly imbalanced data, noisy/incomplete labels, data sampling, applications spanning vision-based industrial inspection, predictive maintenance of complex machines.
Adjacent domains such as in-field inspection and medical diagnosis.
Theoretical contributions that address challenges unique to anomaly detection and novelty detection.

Our workshop will only be accepting full papers.

Full paper submissions to the workshop must be of 8 page papers, with unlimited space for references and supplementary materials, following the CVPR 2025 style and formatting guidelines. The review process is double-blind and there is no rebuttal. Submissions must not have been previously published in a substantially similar form. Accepted papers will be invited for either spotlight talks or poster presentations. Accepted papers will be published in conjunction with CVPR 2025 proceedings.

Submission Portal: https://cmt3.research.microsoft.com/VAND 2025

Important Dates:

Submission deadline: Mar 03, 2025
Author notification: Apr 01, 2025
Camera-ready deadline: Apr 06, 2025
Workshop: TBA (June 11 or 12, 2025)

Please check the Call for Papers page for more details.