1 code implementation • CVPR 2017 • Zheng Shou, Jonathan Chan, Alireza Zareian, Kazuyuki Miyazawa, Shih-Fu Chang
Temporal action localization is an important yet challenging problem.
Ranked #27 on Temporal Action Localization on THUMOS’14 (mAP IOU@0.6 metric)
no code implementations • ECCV 2018 • Zheng Shou, Junting Pan, Jonathan Chan, Kazuyuki Miyazawa, Hassan Mansour, Anthony Vetro, Xavier Giro-i-Nieto, Shih-Fu Chang
We aim to tackle a novel task in action detection - Online Detection of Action Start (ODAS) in untrimmed, streaming videos.
1 code implementation • 22 Jul 2018 • Zheng Shou, Hang Gao, Lei Zhang, Kazuyuki Miyazawa, Shih-Fu Chang
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.
Weakly-supervised Temporal Action Localization Weakly Supervised Temporal Action Localization
no code implementations • ECCV 2018 • Zheng Shou, Hang Gao, Lei Zhang, Kazuyuki Miyazawa, Shih-Fu Chang
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.
Ranked #16 on Weakly Supervised Action Localization on ActivityNet-1.2 (mAP@0.5 metric)
Weakly Supervised Action Localization Weakly-supervised Temporal Action Localization +1