132 citations · 181 across the 10 of their papers we have counts for
10 papers
Kernel Proposal Network for Arbitrary Shape Text Detection
Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou +2
Segmentation-based methods have achieved great success for arbitrary shape text detection. However, separating neighboring text instances is still one of the most challenging probl…
Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification
Zhiyu Fang, Xiaobin Zhu, Chun Yang +3
Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC). However, due…
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…
GiT: Graph Interactive Transformer for Vehicle Re-identification
Fei Shen, Yi Xie, Jianqing Zhu +2
Transformers are more and more popular in computer vision, which treat an image as a sequence of patches and learn robust global features from the sequence. However, pure transform…
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…
Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
Fei Shen, Jianqing Zhu, Xiaobin Zhu +2
Existing vehicle re-identification methods commonly use spatial pooling operations to aggregate feature maps extracted via off-the-shelf backbone networks. They ignore exploring th…
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…
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,…
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…
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…