activity
20192025
most citedEnd-to-end Temporal Action Detection with Transformer

285 citations · 499 across the 33 of their papers we have counts for

collaborators
Showing 2021Show all

13 papers · 1 filter

cs.CV2021★ 31 cited

TAda! Temporally-Adaptive Convolutions for Video Understanding

Ziyuan Huang, Shiwei Zhang, Liang Pan +4

Spatial convolutions are widely used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different…

cs.CV2021★ 7 cited

Support-Set Based Cross-Supervision for Video Grounding

Xinpeng Ding, Nannan Wang, Shiwei Zhang +5

Current approaches for video grounding propose kinds of complex architectures to capture the video-text relations, and have achieved impressive improvements. However, it is hard to…

cs.CV2021★ 1 cited

ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning

Zhiwu Qing, Ziyuan Huang, Shiwei Zhang +5

The central idea of contrastive learning is to discriminate between different instances and force different views from the same instance to share the same representation. To avoid…

cs.CV2021★ 3 cited

Exploring Stronger Feature for Temporal Action Localization

Zhiwu Qing, Xiang Wang, Ziyuan Huang +6

Temporal action localization aims to localize starting and ending time with action category. Limited by GPU memory, mainstream methods pre-extract features for each video. Therefor…

cs.CV2021★ 1 cited

OadTR: Online Action Detection with Transformers

Xiang Wang, Shiwei Zhang, Zhiwu Qing +4

Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure. However, RNN suffers from non-parallelism…

cs.CV2021★ 16 cited

Proposal Relation Network for Temporal Action Detection

Xiang Wang, Zhiwu Qing, Ziyuan Huang +6

This technical report presents our solution for temporal action detection task in AcitivityNet Challenge 2021. The purpose of this task is to locate and identify actions of interes…

cs.CV2021★ 3 cited

Weakly-Supervised Temporal Action Localization Through Local-Global Background Modeling

Xiang Wang, Zhiwu Qing, Ziyuan Huang +6

Weakly-Supervised Temporal Action Localization (WS-TAL) task aims to recognize and localize temporal starts and ends of action instances in an untrimmed video with only video-level…

cs.CV2021★ 8 cited

Relation Modeling in Spatio-Temporal Action Localization

Yutong Feng, Jianwen Jiang, Ziyuan Huang +5

This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021. Our solution utilizes multiple types of relation modeling methods for…

cs.CV2021★ 2 cited

A Stronger Baseline for Ego-Centric Action Detection

Zhiwu Qing, Ziyuan Huang, Xiang Wang +7

This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop. The goal of our task is to…

cs.CV2021★ 8 cited

Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition

Ziyuan Huang, Zhiwu Qing, Xiang Wang +7

With the recent surge in the research of vision transformers, they have demonstrated remarkable potential for various challenging computer vision applications, such as image recogn…

cs.CV2021★ 285 cited

End-to-end Temporal Action Detection with Transformer

Xiaolong Liu, Qimeng Wang, Yao Hu +4

Temporal action detection (TAD) aims to determine the semantic label and the temporal interval of every action instance in an untrimmed video. It is a fundamental and challenging t…

cs.CV2021★ 8 cited

Self-Supervised Learning for Semi-Supervised Temporal Action Proposal

Xiang Wang, Shiwei Zhang, Zhiwu Qing +3

Self-supervised learning presents a remarkable performance to utilize unlabeled data for various video tasks. In this paper, we focus on applying the power of self-supervised metho…

cs.CV2021★ 7 cited

Self-supervised Motion Learning from Static Images

Ziyuan Huang, Shiwei Zhang, Jianwen Jiang +3

Motions are reflected in videos as the movement of pixels, and actions are essentially patterns of inconsistent motions between the foreground and the background. To well distingui…