Search Results for author: Yongchao Xu

Found 19 papers, 16 papers with code

Intra-class Feature Variation Distillation for Semantic Segmentation

1 code implementation ECCV 2020 Yukang Wang, Wei Zhou, Tao Jiang, Xiang Bai, Yongchao Xu

In this paper, different from previous methods performing knowledge distillation for densely pairwise relations, we propose a novel intra-class feature variation distillation (IFVD) to transfer the intra-class feature variation (IFV) of the cumbersome model (teacher) to the compact model (student).

Knowledge Distillation Semantic Segmentation

Local Intensity Order Transformation for Robust Curvilinear Object Segmentation

1 code implementation25 Feb 2022 Tianyi Shi, Nicolas Boutry, Yongchao Xu, Thierry Géraud

This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes.

Semantic Segmentation

Affinity Space Adaptation for Semantic Segmentation Across Domains

1 code implementation26 Sep 2020 Wei Zhou, Yukang Wang, Jiajia Chu, Jiehua Yang, Xiang Bai, Yongchao Xu

Specifically, we perform domain adaptation on the affinity relationship between adjacent pixels termed affinity space of source and target domain.

Semantic Segmentation Unsupervised Domain Adaptation

Learning Directional Feature Maps for Cardiac MRI Segmentation

1 code implementation22 Jul 2020 Feng Cheng, Cheng Chen, Yukang Wang, Heshui Shi, Yukun Cao, Dandan Tu, Changzheng Zhang, Yongchao Xu

Cardiac MRI segmentation plays a crucial role in clinical diagnosis for evaluating personalized cardiac performance parameters.

Cardiac Segmentation MRI segmentation

Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation

1 code implementation CVPR 2020 Jianqiang Wan, Yang Liu, Donglai Wei, Xiang Bai, Yongchao Xu

In this paper, we propose a fast image segmentation method based on a novel super boundary-to-pixel direction (super-BPD) and a customized segmentation algorithm with super-BPD.

BSDS500 Semantic Segmentation +1

AutoSTR: Efficient Backbone Search for Scene Text Recognition

1 code implementation ECCV 2020 Hui Zhang, Quanming Yao, Mingkun Yang, Yongchao Xu, Xiang Bai

In this work, inspired by the success of neural architecture search (NAS), which can identify better architectures than human-designed ones, we propose automated STR (AutoSTR) to search data-dependent backbones to boost text recognition performance.

Deblurring Neural Architecture Search +1

AutoScale: Learning to Scale for Crowd Counting and Localization

2 code implementations20 Dec 2019 Chenfeng Xu, Dingkang Liang, Yongchao Xu, Song Bai, Wei Zhan, Xiang Bai, Masayoshi Tomizuka

A major issue is that the density map on dense regions usually accumulates density values from a number of nearby Gaussian blobs, yielding different large density values on a small set of pixels.

Crowd Counting

Gliding vertex on the horizontal bounding box for multi-oriented object detection

1 code implementation21 Nov 2019 Yongchao Xu, Mingtao Fu, Qimeng Wang, Yukang Wang, Kai Chen, Gui-Song Xia, Xiang Bai

Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts.

Object Detection In Aerial Images Pedestrian Detection +1

All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting

no code implementations21 Nov 2019 Hao Wang, Pu Lu, HUI ZHANG, Mingkun Yang, Xiang Bai, Yongchao Xu, Mengchao He, Yongpan Wang, Wenyu Liu

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.

Instance Segmentation Scene Text Detection +2

Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting

1 code implementation ICCV 2019 Chenfeng Xu, Kai Qiu, Jianlong Fu, Song Bai, Yongchao Xu, Xiang Bai

Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels.

Crowd Counting Density Estimation

TextField: Learning A Deep Direction Field for Irregular Scene Text Detection

1 code implementation4 Dec 2018 Yongchao Xu, Yukang Wang, Wei Zhou, Yongpan Wang, Zhibo Yang, Xiang Bai

Experimental results show that the proposed TextField outperforms the state-of-the-art methods by a large margin (28% and 8%) on two curved text datasets: Total-Text and CTW1500, respectively, and also achieves very competitive performance on multi-oriented datasets: ICDAR 2015 and MSRA-TD500.

Scene Text Detection

DeepFlux for Skeletons in the Wild

1 code implementation CVPR 2019 Yukang Wang, Yongchao Xu, Stavros Tsogkas, Xiang Bai, Sven Dickinson, Kaleem Siddiqi

In the present article, we depart from this strategy by training a CNN to predict a two-dimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms.

Edge Detection Frame +3

Hard-Aware Point-to-Set Deep Metric for Person Re-identification

1 code implementation ECCV 2018 Rui Yu, Zhiyong Dou, Song Bai, Zhao-Xiang Zhang, Yongchao Xu, Xiang Bai

Person re-identification (re-ID) is a highly challenging task due to large variations of pose, viewpoint, illumination, and occlusion.

Metric Learning Person Re-Identification

Deep-Person: Learning Discriminative Deep Features for Person Re-Identification

1 code implementation29 Nov 2017 Xiang Bai, Mingkun Yang, Tengteng Huang, Zhiyong Dou, Rui Yu, Yongchao Xu

Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor.

Person Re-Identification Re-Ranking

Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification

no code implementations15 Apr 2017 Xiang Bai, Mingkun Yang, Pengyuan Lyu, Yongchao Xu, Jiebo Luo

Then, we combine the word embedding of the recognized words and the deep visual features into a single representation, which is optimized by a convolutional neural network for fine-grained image classification.

Classification Fine-Grained Image Classification +1

Hierarchical image simplification and segmentation based on Mumford-Shah-salient level line selection

no code implementations15 Mar 2016 Yongchao Xu, Thierry Géraud, Laurent Najman

Many image simplification and segmentation methods are driven by the optimization of an energy functional, for instance the celebrated Mumford-Shah functional.

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