关键词: 视频动作识别, 视听联合学习, 时间注意力, 深度学习, 长短时记忆循环神经网络

Abstract:

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.

Key words: video action recognition, audio visual joint learning, temporal attention, deep learning, long short-term memory recurrent neural network