# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 2 Related Work  
### Weakly Supervised Video Anomaly Detection  
The weakly supervised setting offered a balanced solution for the VAD task, requiring less annotation efforts than dense frame-level labeling in a fully supervised setting, while achieving superior performance compared to the unsupervised setting. Current WS-VAD approaches can be broadly categorized into two classes [13]. One class includes encoder-agnostic methods, which utilize a pre-trained feature extractor to obtain segment feature and train the classifier to detect abnormal segments. The second category involves encoder-based methods that train both the feature extractor and classifier. In the first category of methods, [PERSON] et al. [7] modeled this task as a multi-instance learning problem, where the segment with the highest abnormal score was used as the video-level prediction, and a ranking loss was employed to increase the margin between normal and abnormal video predictions. [PERSON] et al. [14] proposed a contrastive attention mechanism, assigning importance weights to each video segment and weighting segment features to obtain video-level features, which were then used with video-level labels to train the classifier. [PERSON] et al. [15] improved the loss function by introducing a new inner-bag loss, which encouraged the highest and lowest scores in normal videos to converge as closely as possible, while driving the highest and lowest scores in abnormal videos apart. However, some researchers argued that the feature extractors used in this category were usually pre-trained on large-scale trimmed video datasets, which presented a domain gap when applied to the WS-VAD task [9].  
In the second category of methods, some researchers [5,9,16] performed temporal modeling on the segment features produced by the feature extractor. They used temporal modeling networks to further extract temporal context features and then trained the temporal modeling network alongside the classifier. For example, [PERSON] et al. [5] proposed a multi-scale temporal network (MTN), which captured both multi-resolution local and global temporal dependencies between video segments. [PERSON] and [PERSON] [16] introduced a causal temporal relation (CTR) module, designed to aggregate relevant information from historical and current features through temporal attention. In contrast, [PERSON] et al. [9] trained the feature extractor and classifier simultaneously, without using a temporal modeling network. They proposed an attention-based self-guided encoder to learn task-specific representations and generated clip-level pseudo-labels to guide network optimization. However, both the above two types of methods typically used only a few fixed number of segments with top-\(k\) abnormal scores from both normal and abnormal videos to update the classifier. The classifier obtained by this kind of segment selection and training strategy usually results in biased predictions [10]; that is, only simple and obvious abnormal behaviors can be detected, potentially producing false alarms for more subtle anomalies. Based on this finding, utilizing only video-level labels to achieve unbiased prediction without additional human annotations has become an important problem to address.