VAND 3.0挑战推动视觉异常检测向真实场景演进
From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge
- 设置双赛道，分别应对真实分布偏移与少样本下视觉语言模型能力
- 参赛方法通过融合现有技术显著超越基线，大模型起关键作用
- 适合关注工业落地、少样本学习与实时部署的研究者
视觉异常检测是高度应用驱动的研究领域，学术界与产业界的衔接至关重要。为此，我们推出VAND 3.0挑战赛，展示异常检测在不同实际场景中的进展，并解决该领域的关键问题。挑战包含两个赛道：第一赛道聚焦提升方法对真实世界分布偏移的鲁棒性；第二赛道探索视觉语言模型在少样本条件下的能力。参赛方案通过整合或改进现有方法，并引入新流程，实现了显著性能提升。尽管两大赛道中大规模预训练视觉（语言）主干网络均起到决定性作用，但未来研究仍需更高效地扩展异常检测方法，以满足现场实时性和计算资源限制。
原文摘要 · Abstract (English)
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.
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