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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.17615 |
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| _version_ | 1866911168316047360 |
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| author | Heckler-Kram, Lars Vaidya, Ashwin Neudeck, Jan-Hendrik Scheler, Ulla Ameln, Dick Akcay, Samet Ramos, Paula |
| author_facet | Heckler-Kram, Lars Vaidya, Ashwin Neudeck, Jan-Hendrik Scheler, Ulla Ameln, Dick Akcay, Samet Ramos, Paula |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17615 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge Heckler-Kram, Lars Vaidya, Ashwin Neudeck, Jan-Hendrik Scheler, Ulla Ameln, Dick Akcay, Samet Ramos, Paula Computer Vision and Pattern Recognition 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. |
| title | From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.17615 |