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About Me

I am currently a Senior Researcher at JD Explore Academy. My research interests include multimedia computing, computer vision, generative models for visual contents, and their applications in retail. I received my Ph. D. degree from the Beijing Key Lab of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications in 2018. I received my B.S. degree from Northwest A&F University in 2011.

We are recruiting self-motivated interns in computer vision, deep learning, and multimedia. Please directly send your CV to my email if you are interested in the positions! :D

Recent News

Publications (dblp Google Scholar)










Before 2015


December, 2023, The 19th Young Scientists Conference of the Chinese Society of Image and Graphics (第19届中国图象图形学学会青年科学家会议),优博论坛, “大规模自然场景步态识别初探”(In Chinese).

August, 2022, CCIG 2022 Young Scholar Panel (2022中国图象图形学大会,青年学者论坛), “Gait Recognition from 2D to 3D”. SLIDES

June, 2020, NCIG 2020 Outstanding Doctor and Young Scholar Panel (2020全国图象图形学学术会议,优秀博士与青年学者论坛), “Large-scale Vehicle Search in Smart City (智慧城市中的车辆搜索)” (In Chinese). SLIDES


Area Chair, ICME 2022/2024

Local Session Chair, ACM Multimedia 2021

Proceedings Co-Chair, ACM Multimedia Asia 2021

Co-chair, HUMA Workshop at ACM Multimedia 2020~2023


Conference Reviewer: CVPR, ACM MM, ICCV, ECCV, SIGIR, AAAI, ICME, ICASSP, ICIP, etc.

Membership: CCF/CSIG Senior Member, IEEE/ACM Member.

Awards and Honors

ChinaMM Multimedia Industrial Innovative Technology Award, 2023, for the project “Key Technology on Multi-modal Human Analysis

IEEE CAS MSA-TC Best Paper Award - Honorable Mention, 2022, for the paper “Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

ICCV 2021 DeeperAction Challenge, Track 3 Kinetics-TPS Challenge on Part-level Action Parsing, 2nd Award

IEEE CAS MSA-TC Best Paper Award - Honorable Mention, 2021, for the paper “TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition

Outstanding Doctoral Dissertation Award of China Society of Image and Graphics, 2019, for my Ph. D. thesis “Research on Key Techniques of Vehicle Search in Urban Video Surveillance Networks

IEEE TMM Multimedia Prize Paper Award, 2019, for the paper “PROVID: Progressive and Multimodal Vehicle Reidentification for Large-Scale Urban Surveillance

ICME 2019, 2021, Outstanding Reviewer Award

CVPR 2019 LIP Challenge, Track 3 Multi-Person Human Parsing, 2nd Award

CVPR 2018 LIP Challenge, Track 1 Single-Person Human Parsing, 2nd Award

IEEE ICME Best Student Paper Award, 2016, for the paper “Large-scale vehicle re-identification in urban surveillance videos


Gait Recognition in the Wild with dense 3D Representations (More Details)

Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. However, humans live and walk in the unconstrained 3D space, so projecting the 3D human body onto the 2D plane will discard a lot of crucial information like the viewpoint, shape, and dynamics for gait recognition. Therefore, this paper aims to explore dense 3D representations for gait recognition in the wild, which is a practical yet neglected problem. In particular, we propose a novel framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the human body for gait recognition, named SMPLGait. Our framework has two elaborately-designed branches of which one extracts appearance features from silhouettes, the other learns knowledge of 3D viewpoints and shapes from the 3D SMPL model. With the learned 3D knowledge, the appearance features from arbitrary viewpoints can be normalized in the latent space to overcome the extreme viewpoint changes in the wild scenes. In addition, due to the lack of suitable datasets, we build the first large-scale 3D representation-based gait recognition dataset, named Gait3D. It contains 4,000 subjects and over 25,000 sequences extracted from 39 cameras in an unconstrained indoor scene. More importantly, it provides 3D SMPL models recovered from video frames which can provide dense 3D information of body shape, viewpoint, and dynamics. Furthermore, it also provides 2D silhouettes and keypoints that can be explored for gait recognition using multi-modal data. Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild.


Progressive Vehicle Search in Larve-scale Surveillance Networks (More Details)

Compared with person re-identification, which has concentrated attention, vehicle re-identification is an important yet frontier problem in video surveillance and has been neglected by the multimedia and vision communities. Since most existing approaches mainly consider the general vehicle appearance for re-identification while overlooking the distinct vehicle identifier, such as the license number plate, they attain suboptimal performance. In this work, we propose PROVID, a PROgressive Vehicle re-IDentification framework based on deep neural networks. In particular, our framework not only utilizes the multi-modality data in large-scale video surveillance, such as visual features, license plates, camera locations, and contextual information, but also considers vehicle re-identification in two progressive procedures: coarse-to-fine search in the feature domain, and near-to-distant search in the physical space. Furthermore, to evaluate our progressive search framework and facilitate related research, we construct the VeRi dataset, which is the most comprehensive dataset from real-world surveillance videos. It not only provides large numbers of vehicles with varied labels and sufficient cross-camera recurrences but also contains license number plates and contextual information. Extensive experiments on the VeRi dataset demonstrate both the accuracy and efficiency of our progressive vehicle re-identification framework.

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Multi-grained Vehicle Parsing from Images(More Details)

We present a novel large-scale dataset, Multi-grained Vehicle Parsing (MVP), for semantic analysis of vehicles in the wild, which has several featured properties. First of all, the MVP contains 24,000 vehicle images captured in read-world surveillance scenes, which makes it more scalable than existing datasets. Moreover, for different requirements, we annotate the vehicle images with pixel-level part masks in two granularities, i.e., the coarse annotations of ten classes and the fine annotations of 59 classes. The former can be applied to object-level applications such as vehicle Re-Id, fine-grained classification, and pose estimation, while the latter can be explored for high-quality image generation and content manipulation. Furthermore, the images reflect the complexity of real surveillance scenes, such as different viewpoints, illumination conditions, backgrounds, etc. In addition, the vehicles have diverse countries, types, brands, models, and colors, which makes the dataset more diverse and challenging. A codebase for person and vehicle parsing can be found HERE.


Fine-grained Human Parsing in Images

This paper focuses on fine-grained human parsing in images. This is a very challenging task due to the diverse person’s appearance, semantic ambiguity of different body parts and clothing, and extremely small parsing targets. Although existing approaches can achieve significant improvement through pyramid feature learning, multi-level supervision, and joint learning with pose estimation, human parsing is still far from being solved. Different from existing approaches, we propose a Braiding Network, named BraidNet, to learn complementary semantics and details for fine-grained human parsing. The BraidNet contains a two-stream braid-like architecture. The first stream is a semantic abstracting net with a deep yet narrow structure which can learn semantic knowledge by a hierarchy of fully convolution layers to overcome the challenges of diverse person appearance. To capture low-level details of small targets, the detail-preserving net is designed to exploit a shallow yet wide network without down-sampling, which can retain sufficient local structures for small objects. Moreover, we design a group of braiding modules across the two sub-nets, by which complementary information can be exchanged during end-to-end training. Besides, at the end of BraidNet, a Pairwise Hard Region Embedding strategy is proposed to eliminate the semantic ambiguity of different body parts and clothing. Extensive experiments show that the proposed BraidNet achieves better performance than the state-of-the-art methods for fine-grained human parsing.

Try Human Parsing Online API at JD Neuhub.


Social Relation Recognition in Videos

Discovering social relations, e.g., kinship, friendship, etc., from visual content can make machines better interpret the behaviors and emotions of human beings. Existing studies mainly focus on recognizing social relations from still images while neglecting another important media—video. On the one hand, the actions and storylines in videos provide more important cues for social relation recognition. On the other hand, the key persons may appear at arbitrary spatial-temporal locations, even not in the same image from beginning to end. To overcome these challenges, we propose a Multi-scale Spatial-Temporal Reasoning (MSTR) framework to recognize social relations from videos. For the spatial representation, we not only adopt a temporal segment network to learn global action and scene information but also design a Triple Graphs model to capture visual relations between persons and objects. For the temporal domain, we propose a Pyramid Graph Convolutional Network to perform temporal reasoning with multi-scale receptive fields, which can obtain both long-term and short-term storylines in videos. By this means, MSTR can comprehensively explore the multi-scale actions and storylines in spatial-temporal dimensions for social relation reasoning in videos. Extensive experiments on a new large-scale Video Social Relation dataset demonstrate the effectiveness of the proposed framework. The dataset can be downloaded from BaiduPan (~57GB, download code: jzei).


Last Update: March, 2024