Search Results for author: Haisheng Su

Found 13 papers, 5 papers with code

MVP: Robust Multi-View Practice for Driving Action Localization

no code implementations5 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.

Action Localization

Learning Video Representations of Human Motion From Synthetic Data

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.

Action Recognition Contrastive Learning +2

Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection

1 code implementation7 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

Transferable Knowledge-Based Multi-Granularity Aggregation Network for Temporal Action Localization: Submission to ActivityNet Challenge 2021

no code implementations27 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.

Transfer Learning Weakly-supervised Learning +2

TSI: Temporal Saliency Integration for Video Action Recognition

no code implementations2 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.

Action Recognition Temporal Action Localization

Temporal Context Aggregation Network for Temporal Action Proposal Refinement

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.

Action Detection Retrieval +2

Collaborative Distillation in the Parameter and Spectrum Domains for Video Action Recognition

no code implementations15 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.

Action Recognition Knowledge Distillation +1

Complementary Boundary Generator with Scale-Invariant Relation Modeling for Temporal Action Localization: Submission to ActivityNet Challenge 2020

no code implementations20 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.

Temporal Action Localization

Multi-Granularity Fusion Network for Proposal and Activity Localization: Submission to ActivityNet Challenge 2019 Task 1 and Task 2

no code implementations29 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}).

Re-Ranking Task 2 +1

Cascaded Pyramid Mining Network for Weakly Supervised Temporal Action Localization

no code implementations28 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.

General Classification Video Classification +2

BSN: Boundary Sensitive Network for Temporal Action Proposal Generation

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.

Action Detection Temporal Action Proposal Generation

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