Attentional Fused Temporal Transformation Network for Video Action Recognition
Ke Yang, Zhiyuan Wang, Huadong Dai, Tianlong Shen, Peng Qiao, Xin Niu, Jie Jiang, Dongsheng Li
Abstract
Effective spatiotemporal feature representation is crucial to the video-based action recognition task. Focusing on discriminate spatiotemporal feature learning, we propose Attentional Fused Temporal Transformation Network (AttnTTN) for action recognition on top of popular Temporal Segment Network (TSN) framework. In the network, Attentional Fusion Module (AttnFM) is designed to fuse the appearance and motion features at multiple ConvNet levels for each video snippet, forming a short-term video descriptor. With fused features as inputs, Temporal Transformation Networks (TTN) are employed to model middle-term temporal transformation between the neighboring temporal snippets following a sequential order. AttnTTN achieves the state-of-the-art results on two most popular action recognition datasets: UCF101 and HMDB51.
BibTeX
@inproceedings{icassp2020_attentionalfused,
title = {Attentional Fused Temporal Transformation Network for Video Action Recognition},
author = {Ke Yang and Zhiyuan Wang and Huadong Dai and Tianlong Shen and Peng Qiao and Xin Niu and Jie Jiang and Dongsheng Li and Yong Dou},
booktitle = {ICASSP 2020},
year = {2020}
}