60 citations · 203 across the 22 of their papers we have counts for
7 papers · 1 filter
Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection
Shoubin Yu, Zhongyin Zhao, Haoshu Fang +6
Anomaly detection in surveillance videos is challenging and important for ensuring public security. Different from pixel-based anomaly detection methods, pose-based methods utilize…
Transferable Knowledge-Based Multi-Granularity Aggregation Network for Temporal Action Localization: Submission to ActivityNet Challenge 2021
Haisheng Su, Peiqin Zhuang, Yukun Li +4
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-S…
TSI: Temporal Saliency Integration for Video Action Recognition
Haisheng Su, Kunchang Li, Jinyuan Feng +4
Efficient spatiotemporal modeling is an important yet challenging problem for video action recognition. Existing state-of-the-art methods exploit neighboring feature differences to…
Temporal Context Aggregation Network for Temporal Action Proposal Refinement
Zhiwu Qing, Haisheng Su, Weihao Gan +7
Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field.…
Real-Time Visual Object Tracking via Few-Shot Learning
Jinghao Zhou, Bo Li, Peng Wang +5
Visual Object Tracking (VOT) can be seen as an extended task of Few-Shot Learning (FSL). While the concept of FSL is not new in tracking and has been previously applied by prior wo…
Higher Performance Visual Tracking with Dual-Modal Localization
Jinghao Zhou, Bo Li, Lei Qiao +5
Visual Object Tracking (VOT) has synchronous needs for both robustness and accuracy. While most existing works fail to operate simultaneously on both, we investigate in this work t…
Learning Statistical Texture for Semantic Segmentation
Lanyun Zhu, Deyi Ji, Shiping Zhu +3
Existing semantic segmentation works mainly focus on learning the contextual information in high-level semantic features with CNNs. In order to maintain a precise boundary, low-lev…