Xinggang Wang

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Huazhong University of Science u0026 Technology

Xinggang Wang is an Associate Professor in the School of Electronic Information and Communications in Huazhong University of Science and Technology

. His research interests are computer vision and machine learning, especially,

data/label- and computation-efficient deep learning for visual recognition
.

Fast Neural Network Adaptation via Parameter Remapping and Architecture Search

Deep neural networks achieve remarkable performance in many computer vis...
01/08/2020 ∙ by Jiemin Fang, et al. ∙ 26 ∙ share

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Mask Scoring R-CNN

Letting a deep network be aware of the quality of its own predictions is...
03/01/2019 ∙ by Zhaojin Huang, et al. ∙ 12 ∙ share

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Diversity Transfer Network for Few-Shot Learning

Few-shot learning is a challenging task that aims at training a classifi...
12/31/2019 ∙ by Mengting Chen, et al. ∙ 12 ∙ share

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Direct Object Recognition Without Line-of-Sight Using Optical Coherence

Visual object recognition under situations in which the direct line-of-s...
03/18/2019 ∙ by Xin Lei, et al. ∙ 10 ∙ share

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IoU-aware Single-stage Object Detector for Accurate Localization

Due to the simpleness and high efficiency, single-stage object detectors...
12/12/2019 ∙ by Shengkai Wu, et al. ∙ 9 ∙ share

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EAT-NAS: Elastic Architecture Transfer for Accelerating Large-scale Neural Architecture Search

Neural architecture search (NAS) methods have been proposed to release h...
01/17/2019 ∙ by Jiemin Fang, et al. ∙ 8 ∙ share

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Object Detection in Video with Spatial-temporal Context Aggregation

Recent cutting-edge feature aggregation paradigms for video object detec...
07/11/2019 ∙ by Hao Luo, et al. ∙ 7 ∙ share

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Detect or Track: Towards Cost-Effective Video Object Detection/Tracking

State-of-the-art object detectors and trackers are developing fast. Trac...
11/13/2018 ∙ by Hao Luo, et al. ∙ 6 ∙ share

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CCNet: Criss-Cross Attention for Semantic Segmentation

Long-range dependencies can capture useful contextual information to ben...
11/28/2018 ∙ by Zilong Huang, et al. ∙ 6 ∙ share

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High-Resolution Representations for Labeling Pixels and Regions

High-resolution representation learning plays an essential role in many ...
04/09/2019 ∙ by Ke Sun, et al. ∙ 6 ∙ share

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Densely Connected Search Space for More Flexible Neural Architecture Search

In recent years, neural architecture search (NAS) has dramatically advan...
06/23/2019 ∙ by Jiemin Fang, et al. ∙ 6 ∙ share

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Unsupervised Domain Adaptive Re-Identification: Theory and Practice

We study the problem of unsupervised domain adaptive re-identification (...
07/30/2018 ∙ by Liangchen Song, et al. ∙ 4 ∙ share

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Proposal, Tracking and Segmentation (PTS): A Cascaded Network for Video Object Segmentation

Video object segmentation (VOS) aims at pixel-level object tracking give...
07/02/2019 ∙ by Qiang Zhou, et al. ∙ 4 ∙ share

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Reducing the Dilution: analysis of the information sensitiveness of capsule network and one practical solution

Capsule network has shown various advantages over convolutional neural n...
03/25/2019 ∙ by Zonglin Yang, et al. ∙ 2 ∙ share

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Reducing the dilution: An analysis of the information sensitiveness of capsule network with a practical solution

Capsule network has shown various advantages over convolutional neural n...
03/25/2019 ∙ by Zonglin Yang, et al. ∙ 2 ∙ share

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Point Linking Network for Object Detection

Object detection is a core problem in computer vision. With the developm...
06/12/2017 ∙ by Xinggang Wang, et al. ∙ 0 ∙ share

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Deep Patch Learning for Weakly Supervised Object Classification and Discovery

Patch-level image representation is very important for object classifica...
05/06/2017 ∙ by Peng Tang, et al. ∙ 0 ∙ share

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Multiple Instance Detection Network with Online Instance Classifier Refinement

Of late, weakly supervised object detection is with great importance in ...
04/01/2017 ∙ by Peng Tang, et al. ∙ 0 ∙ share

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Revisiting Multiple Instance Neural Networks

Recently neural networks and multiple instance learning are both attract...
10/08/2016 ∙ by Xinggang Wang, et al. ∙ 0 ∙ share

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TextBoxes: A Fast Text Detector with a Single Deep Neural Network

This paper presents an end-to-end trainable fast scene text detector, na...
11/21/2016 ∙ by Minghui Liao, et al. ∙ 0 ∙ share

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Bag Reference Vector for Multi-instance Learning

Multi-instance learning (MIL) has a wide range of applications due to it...
12/03/2015 ∙ by Hanqiang Song, et al. ∙ 0 ∙ share

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Deep FisherNet for Object Classification

Despite the great success of convolutional neural networks (CNN) for the...
07/31/2016 ∙ by Peng Tang, et al. ∙ 0 ∙ share

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Shape Recognition by Bag of Skeleton-associated Contour Parts

Contour and skeleton are two complementary representations for shape rec...
05/20/2016 ∙ by Wei Shen, et al. ∙ 0 ∙ share

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Robust Scene Text Recognition with Automatic Rectification

Recognizing text in natural images is a challenging task with many unsol...
03/12/2016 ∙ by Baoguang Shi, et al. ∙ 0 ∙ share

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Relaxed Multiple-Instance SVM with Application to Object Discovery

Multiple-instance learning (MIL) has served as an important tool for a w...
10/05/2015 ∙ by Xinggang Wang, et al. ∙ 0 ∙ share

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Deep Learning Representation using Autoencoder for 3D Shape Retrieval

We study the problem of how to build a deep learning representation for ...
09/25/2014 ∙ by Zhuotun Zhu, et al. ∙ 0 ∙ share

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Object Detection in Videos by Short and Long Range Object Linking

We address the problem of detecting objects in videos with the interest ...
01/30/2018 ∙ by Peng Tang, et al. ∙ 0 ∙ share

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PCL: Proposal Cluster Learning for Weakly Supervised Object Detection

Weakly Supervised Object Detection (WSOD), using only image-level annota...
07/09/2018 ∙ by Peng Tang, et al. ∙ 0 ∙ share

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Reinforced Evolutionary Neural Architecture Search

Neural architecture search (NAS) is an important task in network design,...
08/01/2018 ∙ by Yukang Chen, et al. ∙ 0 ∙ share

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Deep High-Resolution Representation Learning for Visual Recognition

High-resolution representations are essential for position-sensitive vis...
08/20/2019 ∙ by Jingdong Wang, et al. ∙ 0 ∙ share

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Xinggang Wang

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