Papers › Overlooked Video Classification in Weakly Supervised Video Anomaly Detection

Overlooked Video Classification in Weakly Supervised Video Anomaly Detection

13 Oct 2022arXiv:2210.06688archive 2025-07-28

Weijun Tan, Qi Yao, Jingfeng Liu

Current weakly supervised video anomaly detection algorithms mostly use multiple instance learning (MIL) or their varieties. Almost all recent approaches focus on how to select the correct snippets for training to improve the performance. They overlook or do not realize the power of video classification in boosting the performance of anomaly detection. In this paper, we study explicitly the power of video classification supervision using a BERT or LSTM. With this BERT or LSTM, CNN features of all snippets of a video can be aggregated into a single feature which can be used for video classification. This simple yet powerful video classification supervision, combined into the MIL framework, brings extraordinary performance improvement on all three major video anomaly detection datasets. Particularly it improves the mean average precision (mAP) on the XD-Violence from SOTA 78.84\% to new 82.10\%. The source code is available at https://github.com/wjtan99/BERT_Anomaly_Video_Classification.

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Code

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Tasks

All Anomaly Detection Classification Multiple Instance Learning Video Classification Weakly-supervised Video Anomaly Detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Weakly-supervised Video Anomaly Detection	ShanghaiTech Weakly Supervised	RTFM-BERT	AUC-ROC	97.54	#6 of 16	Archive leaderboard	report

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Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout LSTM Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Sigmoid Activation Softmax Tanh Activation Weight Decay WordPiece

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