1k citations · 2.1k across the 21 of their papers we have counts for
21 papers
Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
Zhihao Li, Jiancheng Yang, Yongchao Xu +3
Pulmonary nodules and masses are crucial imaging features in lung cancer screening that require careful management in clinical diagnosis. Despite the success of deep learning-based…
Not All Pixels Are Equal: Learning Pixel Hardness for Semantic Segmentation
Xin Xiao, Daiguo Zhou, Jiagao Hu +2
Semantic segmentation has recently witnessed great progress. Despite the impressive overall results, the segmentation performance in some hard areas (e.g., small objects or thin pa…
Local Intensity Order Transformation for Robust Curvilinear Object Segmentation
Tianyi Shi, Nicolas Boutry, Yongchao Xu +1
Segmentation of curvilinear structures is important in many applications, such as retinal blood vessel segmentation for early detection of vessel diseases and pavement crack segmen…
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence
Xiang Bai, Hanchen Wang, Liya Ma +43
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection…
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Dawei Du, Longyin Wen, Pengfei Zhu +52
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.…
Affinity Space Adaptation for Semantic Segmentation Across Domains
Wei Zhou, Yukang Wang, Jiajia Chu +3
Semantic segmentation with dense pixel-wise annotation has achieved excellent performance thanks to deep learning. However, the generalization of semantic segmentation in the wild…
Learning Directional Feature Maps for Cardiac MRI Segmentation
Feng Cheng, Cheng Chen, Yukang Wang +5
Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters. Due to the indistinct boundaries and heterogeneous i…
Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation
Jianqiang Wan, Yang Liu, Donglai Wei +2
Image segmentation is a fundamental vision task and a crucial step for many applications. In this paper, we propose a fast image segmentation method based on a novel super boundary…
AutoSTR: Efficient Backbone Search for Scene Text Recognition
Hui Zhang, Quanming Yao, Mingkun Yang +2
Scene text recognition (STR) is very challenging due to the diversity of text instances and the complexity of scenes. The community has paid increasing attention to boost the perfo…
AutoScale: Learning to Scale for Crowd Counting and Localization
Chenfeng Xu, Dingkang Liang, Yongchao Xu +4
Recent works on crowd counting mainly leverage CNNs to count by regressing density maps, and have achieved great progress. In the density map, each person is represented by a Gauss…
All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting
Hao Wang, Pu Lu, Hui Zhang +6
Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Differ…
Gliding vertex on the horizontal bounding box for multi-oriented object detection
Yongchao Xu, Mingtao Fu, Qimeng Wang +4
Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects su…
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting
Chenfeng Xu, Kai Qiu, Jianlong Fu +3
Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels. Existing approaches mainly suffer from…
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser +41
Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are o…
TextField: Learning A Deep Direction Field for Irregular Scene Text Detection
Yongchao Xu, Yukang Wang, Wei Zhou +3
Scene text detection is an important step of scene text reading system. The main challenges lie on significantly varied sizes and aspect ratios, arbitrary orientations and shapes.…
DeepFlux for Skeletons in the Wild
Yukang Wang, Yongchao Xu, Stavros Tsogkas +3
Computing object skeletons in natural images is challenging, owing to large variations in object appearance and scale, and the complexity of handling background clutter. Many recen…
Hard-Aware Point-to-Set Deep Metric for Person Re-identification
Rui Yu, Zhiyong Dou, Song Bai +3
Person re-identification (re-ID) is a highly challenging task due to large variations of pose, viewpoint, illumination, and occlusion. Deep metric learning provides a satisfactory…
Deep-Person: Learning Discriminative Deep Features for Person Re-Identification
Xiang Bai, Mingkun Yang, Tengteng Huang +3
Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor. However, the spatial context b…
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification
Xiang Bai, Mingkun Yang, Pengyuan Lyu +2
Text in natural images contains rich semantics that are often highly relevant to objects or scene. In this paper, we focus on the problem of fully exploiting scene text for visual…
Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection
Yongchao Xu, Thierry Géraud, Laurent Najman
Hierarchies, such as the tree of shapes, are popular representations for image simplification and segmentation thanks to their multiscale structures. Selecting meaningful level lin…
Morphological Filtering in Shape Spaces: Applications using Tree-Based Image Representations
Yongchao Xu, Thierry Géraud, Laurent Najman
Connected operators are filtering tools that act by merging elementary regions of an image. A popular strategy is based on tree-based image representations: for example, one can co…