Papers › Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model

Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model

4 Feb 2022arXiv:2202.02212archive 2025-07-28

Hamid Mohammadi, Ehsan Nazerfard

The significant growth of surveillance camera networks necessitates scalable AI solutions to efficiently analyze the large amount of video data produced by these networks. As a typical analysis performed on surveillance footage, video violence detection has recently received considerable attention. The majority of research has focused on improving existing methods using supervised methods, with little, if any, attention to the semi-supervised learning approaches. In this study, a reinforcement learning model is introduced that can outperform existing models through a semi-supervised approach. The main novelty of the proposed method lies in the introduction of a semi-supervised hard attention mechanism. Using hard attention, the essential regions of videos are identified and separated from the non-informative parts of the data. A model's accuracy is improved by removing redundant data and focusing on useful visual information in a higher resolution. Implementing hard attention mechanisms using semi-supervised reinforcement learning algorithms eliminates the need for attention annotations in video violence datasets, thus making them readily applicable. The proposed model utilizes a pre-trained I3D backbone to accelerate and stabilize the training process. The proposed model achieved state-of-the-art accuracy of 90.4% and 98.7% on RWF and Hockey datasets, respectively.

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Tasks

Activity Recognition Hard Attention Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Activity Recognition	RWF-2000	Semi-Supervised Hard Attention (SSHA); pretrained on Deepmind Kinetics dataset	Accuracy	90.4	#2 of 6	Archive leaderboard	report

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