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• W4312794093 abstract "Violent video recognition is a challenging task in the field of computer vision and multimodal methods have always been an important part of it. Due to containing sensitive content, it is not easy to collect violent videos and resulting in a lack of big public datasets. Existing methods of learning violent video representations are limited by small datasets and lack efficient multimodal fusion models. According to the situation, firstly, we propose to effectively transfer information from large datasets to small violent datasets based on mutual distillation with the self-supervised pretrained model for the vital RGB feature. Secondly, the multimodal attention fusion network (MAF-Net) is proposed to fuse the obtained RGB feature with flow and audio feature to recognize violent videos with multi-modal information. Thirdly, we build a new violent dataset, named Violent Clip Dataset (VCD), which is on a large scale and contains complete audio information. We performed experiments on the public VSD dataset and the self-built VCD dataset. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods on both datasets." @default.
• W4312794093 created "2023-01-05" @default.
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• W4312794093 date "2022-01-01" @default.
• W4312794093 modified "2023-09-29" @default.
• W4312794093 title "Multimodal Violent Video Recognition Based on Mutual Distillation" @default.
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• W4312794093 doi "https://doi.org/10.1007/978-3-031-18913-5_48" @default.
• W4312794093 hasPublicationYear "2022" @default.
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