activity
20182021
most citedAdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization

8 citations · 14 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV20211 cited

Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection

Shi-Xue Zhang, Xiaobin Zhu, Chun Yang +2

Arbitrary shape text detection is a challenging task due to the high complexity and variety of scene texts. In this work, we propose a novel adaptive boundary proposal network for…

cs.CV2020

Two-Stage Copy-Move Forgery Detection with Self Deep Matching and Proposal SuperGlue

Yaqi Liu, Chao Xia, Xiaobin Zhu +1

Copy-move forgery detection identifies a tampered image by detecting pasted and source regions in the same image. In this paper, we propose a novel two-stage framework specially fo…

cs.CV2020

Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection

Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou +4

Arbitrary shape text detection is a challenging task due to the high variety and complexity of scenes texts. In this paper, we propose a novel unified relational reasoning graph ne…

cs.CV20198 cited

AdapNet: Adaptability Decomposing Encoder-Decoder Network for Weakly Supervised Action Recognition and Localization

Xiao-Yu Zhang, Changsheng Li, Haichao Shi +3

The point process is a solid framework to model sequential data, such as videos, by exploring the underlying relevance. As a challenging problem for high-level video understanding,…

cs.CV20195 cited

Learning Transferable Self-attentive Representations for Action Recognition in Untrimmed Videos with Weak Supervision

Xiao-Yu Zhang, Haichao Shi, Changsheng Li +3

Action recognition in videos has attracted a lot of attention in the past decade. In order to learn robust models, previous methods usually assume videos are trimmed as short seque…

cs.CV2018

Adversarial Learning for Image Forensics Deep Matching with Atrous Convolution

Yaqi Liu, Xianfeng Zhao, Xiaobin Zhu +1

Constrained image splicing detection and localization (CISDL) is a newly proposed challenging task for image forensics, which investigates two input suspected images and identifies…