基于关键帧筛选网络的视听联合动作识别

近年来，视听联合学习的动作识别获得了一定关注。无论在视频（视觉模态）还是音频（听觉模态）中，动作发生是瞬时的，往往在动作发生时间段内的信息才能够显著地表达动作类别。如何更好地利用视听模态的关键帧携带的显著表达动作信息，是视听动作识别待解决的问题之一。针对该问题，提出关键帧筛选网络KFIA-S，通过基于全连接层的线性时间注意力机制赋予每个时刻视听信息不同权重，从而筛选益于视频分类的视听特征，减少重复冗余信息，抑制背景干扰信息，提升动作识别精度。研究了不同强度的时间注意力对动作识别的影响。在ActivityNet数据集上的实验表明，KFIA-S网络达到了最先进的识别精度，证明了所提方法的有效性。

Audio visual joint action recognition based on key frame selection network

In recent years， the action recognition of audio visual joint learning has received some attention. Whether in video （visual modality） or audio （auditory modality）， the occurrence of action is instantaneous， only the information in the time period of action can significantly express the action category. How to make better use of the significant expression information carried by the key frames of audio-visual modality is one of the problems to be solved in audio-visual action recognition. According to this problem， a key frame screening network KFIA-S was proposed. Though the linear temporal attention mechanism based on the full connected layer， different weights were given to the audio-visual information at different times， so as to screen the audio-visual features beneficial to video classification， reduce redundant information， suppress background interference information， and improve the accuracy of action recognition. The effect of different intensity of time attention on action recognition was studied. The experiments on ActivityNet dataset show that KFIA-S network achieves the SOTA （State-Of-The-Art） recognition accuracy， which proves the effectiveness of the proposed method.