Details

Autor(en) / Beteiligte

• Song, Lin
• Zhang, Shiwei
• Yu, Gang
• Sun, Hongbin

Titel

TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection

Ist Teil von

• 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, p.11979-11987

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Link zum Volltext

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Current state-of-the-art approaches for spatio-temporal action detection have achieved impressive results but remain unsatisfactory for temporal extent detection. The main reason comes from that, there are some ambiguous states similar to the real actions which may be treated as target actions even by a well trained network. In this paper, we define these ambiguous samples as "transitional states", and propose a Transition-Aware Context Network (TACNet) to distinguish transitional states. The proposed TACNet includes two main components, i.e., temporal context detector and transition-aware classifier. The temporal context detector can extract long-term context information with constant time complexity by constructing a recurrent network. The transition-aware classifier can further distinguish transitional states by classifying action and transitional states simultaneously. Therefore, the proposed TACNet can substantially improve the performance of spatio-temporal action detection. We extensively evaluate the proposed TACNet on UCF101-24 and J-HMDB datasets. The experimental results demonstrate that TACNet obtains competitive performance on JHMDB and significantly outperforms the state-of-the-art methods on the untrimmed UCF101 24 in terms of both frame-mAP and video-mAP.