mlsanityData to 14 Oct 2026

From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge

arXiv 2509.17615 ↗First posted 22 Sep 2025cs.CVcited by 2

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Abstract (arXiv)

Visual anomaly detection is a strongly application-driven field of research. Consequently, the connection between academia and industry is of paramount importance. In this regard, we present the VAND 3.0 Challenge to showcase current progress in anomaly detection across different practical settings whilst addressing critical issues in the field. The challenge hosted two tracks, fostering the development of anomaly detection methods robust against real-world distribution shifts (Category 1) and exploring the capabilities of Vision Language Models within the few-shot regime (Category 2), respectively. The participants' solutions reached significant improvements over previous baselines by combining or adapting existing approaches and fusing them with novel pipelines. While for both tracks the progress in large pre-trained vision (language) backbones played a pivotal role for the performance increase, scaling up anomaly detection methods more efficiently needs to be addressed by future research to meet real-time and computational constraints on-site.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 33 of its 43 references that are in the map. First posted on arXiv 22 Sep 2025.

Builds on

33 of 43 references.

References · 33

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21 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026SuperADD: Training-free Class-agnostic Anomaly Segmentation -- CVPR 2026 VAND 4.0 Workshop Challenge Industrial Trackcited by 1
  2. 2026Detecting Defects that Matter: An Application-Driven Benchmark for Anomaly Detection in Manufacturing and Retail Logistics (VAND 4.0 Challenge)cited by 0

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