Conference
Few-Shot Learning of Video Action Recognition Only Based on Video Contents
Abstract
The success of video action recognition based on Deep Neural Networks (DNNs) is highly dependent on a large number of manually labeled videos. In this paper, we introduce a supervised learning approach to recognize video actions with very few training videos. Specifically, we propose Temporal Attention Vectors (TAVs) which adapt various length videos to preserve the temporal information of the entire video. We evaluate the TAVs on UCF101 and …
Authors
Bo Y; Lu Y; He W
Volume
00
Pagination
pp. 584-593
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publication Date
March 5, 2020
DOI
10.1109/wacv45572.2020.9093481
Name of conference
2020 IEEE Winter Conference on Applications of Computer Vision (WACV)