Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection

Weakly-supervised Video Anomaly Detection (wVAD) aims to detect frame-level anomalies using only video-level labels in training. Due to the limitation of coarse-grained labels, Multi-Instance Learning (MIL) is prevailing in wVAD. However, MIL suffers from insufficiency of binary supervision to model diverse abnormal patterns. Besides, the coupling between abnormality and its context hinders the learning of clear abnormal event boundary. In this paper, we propose prompt-enhanced MIL to detect various abnormal events while ensuring clear event boundaries. Concretely, we design the abnormal-aware prompts by using abnormal class annotations together with learnable prompt, which can incorporate semantic priors into video features dynamically. The detector can utilize the semantic-rich features to capture diverse abnormal patterns. In addition, normal context prompt is introduced to amplify the distinction between abnormality and its context, facilitating the generation of clear boundary. With the mutual enhancement of abnormal-aware and normal context prompt, the model can construct discriminative representations to detect divergent anomalies without ambiguous event boundaries. Extensive experiments demonstrate our method achieves SOTA performance on three public benchmarks. The code is available at https://github.com/Junxi-Chen/PE-MIL.