The field of anomaly detection and vision-language models is rapidly evolving, with a focus on developing more robust and adaptable systems. Recent research has emphasized the importance of context-aware and open-world approaches, allowing models to generalize to previously unseen scenarios and adapt to changing environments. Notably, contrastive learning and meta-learning have emerged as key techniques for improving model performance and robustness.

Several papers have made significant contributions to the field, including the development of novel frameworks for anomaly detection, such as those utilizing semantic alignment and self-generated anomaly samples. Others have focused on improving the performance of vision-language models in open-world settings, leveraging techniques like label smoothing and active labeling.

Some noteworthy papers include: OASIS, which proposes a method for handling open-world problems even when pre-training is conducted on imbalanced data. Context-Aware Zero-Shot Anomaly Detection in Surveillance, which introduces a novel framework that identifies abnormal events without exposure to anomaly examples during training. Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework, which constructs a category-agnostic representation space via contrastive learning, enabling anomaly detection with few shots.