1.3k citations · 1.5k across the 15 of their papers we have counts for
14 papers · 1 filter
DDT: Dual-branch Deformable Transformer for Image Denoising
Kangliang Liu, Xiangcheng Du, Sijie Liu +3
Transformer is beneficial for image denoising tasks since it can model long-range dependencies to overcome the limitations presented by inductive convolutional biases. However, dir…
Aggregated Text Transformer for Scene Text Detection
Zhao Zhou, Xiangcheng Du, Yingbin Zheng +1
This paper explores the multi-scale aggregation strategy for scene text detection in natural images. We present the Aggregated Text TRansformer(ATTR), which is designed to represen…
Progressive Scene Text Erasing with Self-Supervision
Xiangcheng Du, Zhao Zhou, Yingbin Zheng +3
Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data. Although data syntheti…
Cross-Domain Document Layout Analysis Using Document Style Guide
Xingjiao Wu, Luwei Xiao, Xiangcheng Du +5
The document layout analysis (DLA) aims to decompose document images into high-level semantic areas (i.e., figures, tables, texts, and background). Creating a DLA framework with st…
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images
Zhichao Fu, Yu Kong, Yingbin Zheng +4
The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results. Among these scenarios, the…
Scene Text Recognition with Temporal Convolutional Encoder
Xiangcheng Du, Tianlong Ma, Yingbin Zheng +3
Texts from scene images typically consist of several characters and exhibit a characteristic sequence structure. Existing methods capture the structure with the sequence-to-sequenc…
Edge-Aware Deep Image Deblurring
Zhichao Fu, Tianlong Ma, Yingbin Zheng +3
Image deblurring is a fundamental and challenging low-level vision problem. Previous vision research indicates that edge structure in natural scenes is one of the most important fa…
Fast Video Crowd Counting with a Temporal Aware Network
Xingjiao Wu, Baohan Xu, Yingbin Zheng +3
Crowd counting aims to count the number of instantaneous people in a crowded space, and many promising solutions have been proposed for single image crowd counting. With the ubiqui…
Detecting Curve Text with Local Segmentation Network and Curve Connection
Zhao Zhou, Hao Ye, Luhui Chen +1
Curve text or arbitrary shape text is very common in real-world scenarios. In this paper, we propose a novel framework with the local segmentation network (LSN) followed by the cur…
Adaptive Scenario Discovery for Crowd Counting
Xingjiao Wu, Yingbin Zheng, Hao Ye +3
Crowd counting, i.e., estimation number of the pedestrian in crowd images, is emerging as an important research problem with the public security applications. A key component for t…
Crowd Counting with Density Adaption Networks
Li Wang, Weiyuan Shao, Yao Lu +3
Crowd counting is one of the core tasks in various surveillance applications. A practical system involves estimating accurate head counts in dynamic scenarios under different light…
Precise Temporal Action Localization by Evolving Temporal Proposals
Haonan Qiu, Yingbin Zheng, Hao Ye +3
Locating actions in long untrimmed videos has been a challenging problem in video content analysis. The performances of existing action localization approaches remain unsatisfactor…
Evolving Boxes for Fast Vehicle Detection
Li Wang, Yao Lu, Hong Wang +3
We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes…
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
Jianqi Ma, Weiyuan Shao, Hao Ye +4
This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images. We present the Rotation Region Proposal Networks (RRPN), which…