1 code implementation • 14 Nov 2022 • Yong-Lu Li, Hongwei Fan, Zuoyu Qiu, Yiming Dou, Liang Xu, Hao-Shu Fang, Peiyang Guo, Haisheng Su, Dongliang Wang, Wei Wu, Cewu Lu
In daily HOIs, humans often interact with a variety of objects, e. g., holding and touching dozens of household items in cleaning.
no code implementations • 5 Jul 2022 • Jingjie Shang, Kunchang Li, Kaibin Tian, Haisheng Su, Yangguang Li
Due to the small data scale and unclear action boundary, the dataset presents a unique challenge to precisely localize all the different actions and classify their categories.
no code implementations • CVPR 2022 • Xi Guo, Wei Wu, Dongliang Wang, Jing Su, Haisheng Su, Weihao Gan, Jian Huang, Qin Yang
In this paper, we take an early step towards video representation learning of human actions with the help of largescale synthetic videos, particularly for human motion representation enhancement.
1 code implementation • 7 Dec 2021 • Shoubin Yu, Zhongyin Zhao, Haoshu Fang, Andong Deng, Haisheng Su, Dongliang Wang, Weihao Gan, Cewu Lu, Wei Wu
Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational burden and also avoids the negative impact of background noise.
Anomaly Detection In Surveillance Videos Optical Flow Estimation +1
no code implementations • 27 Jul 2021 • Haisheng Su, Peiqin Zhuang, Yukun Li, Dongliang Wang, Weihao Gan, Wei Wu, Yu Qiao
This technical report presents an overview of our solution used in the submission to 2021 HACS Temporal Action Localization Challenge on both Supervised Learning Track and Weakly-Supervised Learning Track.
no code implementations • 2 Jun 2021 • Haisheng Su, Jinyuan Feng, Dongliang Wang, Weihao Gan, Wei Wu, Yu Qiao
Specifically, SME aims to highlight the motion-sensitive area through local-global motion modeling, where the saliency alignment and pyramidal feature difference are conducted successively between neighboring frames to capture motion dynamics with less noises caused by misaligned background.
1 code implementation • CVPR 2021 • Zhiwu Qing, Haisheng Su, Weihao Gan, Dongliang Wang, Wei Wu, Xiang Wang, Yu Qiao, Junjie Yan, Changxin Gao, Nong Sang
In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement.
Ranked #9 on Temporal Action Localization on ActivityNet-1.3
1 code implementation • 15 Sep 2020 • Haisheng Su, Weihao Gan, Wei Wu, Yu Qiao, Junjie Yan
In this paper, we present BSN++, a new framework which exploits complementary boundary regressor and relation modeling for temporal proposal generation.
no code implementations • 15 Sep 2020 • Haisheng Su, Jing Su, Dongliang Wang, Weihao Gan, Wei Wu, Mengmeng Wang, Junjie Yan, Yu Qiao
Second, the parameter frequency distribution is further adopted to guide the student network to learn the appearance modeling process from the teacher.
no code implementations • 20 Jul 2020 • Haisheng Su, Jinyuan Feng, Hao Shao, Zhenyu Jiang, Manyuan Zhang, Wei Wu, Yu Liu, Hongsheng Li, Junjie Yan
Specifically, in order to generate high-quality proposals, we consider several factors including the video feature encoder, the proposal generator, the proposal-proposal relations, the scale imbalance, and ensemble strategy.
no code implementations • 29 Jul 2019 • Haisheng Su, Xu Zhao, Shuming Liu
This technical report presents an overview of our solution used in the submission to ActivityNet Challenge 2019 Task 1 (\textbf{temporal action proposal generation}) and Task 2 (\textbf{temporal action localization/detection}).
no code implementations • 28 Oct 2018 • Haisheng Su, Xu Zhao, Tianwei Lin
Weakly supervised temporal action localization, which aims at temporally locating action instances in untrimmed videos using only video-level class labels during training, is an important yet challenging problem in video analysis.
17 code implementations • ECCV 2018 • Tianwei Lin, Xu Zhao, Haisheng Su, Chongjing Wang, Ming Yang
Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content.
Ranked #3 on Temporal Action Proposal Generation on THUMOS' 14