Jingdong Wang

Microsoft Research Asia, Beijing, China
0000-0002-4888-4445
https://jingdongwang2017.github.io/
Publications236
h-index66
Citations22,771
Highly Influential Citations3,139
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  • Publications
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Scalable Person Re-identification: A Benchmark
TLDR
A minor contribution, inspired by recent advances in large-scale image search, an unsupervised Bag-of-Words descriptor is proposed that yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large- scale 500k dataset.
Learning to Detect a Salient Object
TLDR
A set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, are proposed to describe a salient object locally, regionally, and globally.
Deep High-Resolution Representation Learning for Human Pose Estimation
TLDR
This paper proposes a network that maintains high-resolution representations through the whole process of human pose estimation and empirically demonstrates the effectiveness of the network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.
MARS: A Video Benchmark for Large-Scale Person Re-Identification
TLDR
It is shown that CNN in classification mode can be trained from scratch using the consecutive bounding boxes of each identity, and the learned CNN embedding outperforms other competing methods considerably and has good generalization ability on other video re-id datasets upon fine-tuning.
Deep High-Resolution Representation Learning for Visual Recognition
TLDR
The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
Salient Object Detection: A Discriminative Regional Feature Integration Approach
TLDR
This paper presents a principled extension, supervised feature integration, which learns a random forest regressor to discriminatively integrate the saliency features for saliency computation and significantly outperforms state-of-the-art methods on seven benchmark datasets.
High-Resolution Representations for Labeling Pixels and Regions
TLDR
A simple modification is introduced to augment the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions rather than only the representation from thehigh-resolution convolution, which leads to stronger representations, evidenced by superior results.
MMDetection: Open MMLab Detection Toolbox and Benchmark
TLDR
This paper presents MMDetection, an object detection toolbox that contains a rich set of object detection and instance segmentation methods as well as related components and modules, and conducts a benchmarking study on different methods, components, and their hyper-parameters.
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
TLDR
This paper proposes a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (Whole saliency maps) and presents a graph Laplacian regularized nonlinear regression model for saliency refinement.
Automatic salient object segmentation based on context and shape prior
TLDR
A novel automatic salient object segmentation algorithm which integrates both bottom-up salient stimuli and object-level shape prior, leading to binary segmentation of the salient object.
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