Jianbing Shen

Beijing Institute of Technology, Computer Science, Faculty Member

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He is a Full Professor in Department of Computer Science and Technology, Beijing Institute of Technology (BIT) since from June 2013. In September 2007, He obtained his Ph.D. from the Department of Computer Science ( State Key Lab of CAD & CG), Zhejiang University. His current research interests are energy optimization for visual computing. He has published about 50 journal and conference papers such as IEEE TIP, IEEE TMM, IEEE CSVT, IEEE TCYB, IEEE CVPR, IEEE ICCV. He also was the program committee member of over 10+ international conferences like ACM Multimedia. He has also obtained many flagship honors including the Fok Ying Tung Education Foundation from Ministry of Education, the Program for Beijing Excellent Youth Talents from Beijing Municipal Education Commission, and the Program for New Century Excellent Talents in University from Ministry of Education. He is on the editorial boards of Elsevier Journal Neurocomputing, and he is a Senior Member of IEEE and a member of ACM.

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Papers by Jianbing Shen

Active Perception for Visual-Language Navigation

International Journal of Computer Vision

CenterFormer: Center-Based Transformer for 3D Object Detection

Lecture Notes in Computer Science

V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

Lecture Notes in Computer Science

Human-Aware Motion Deblurring

Cornell University - arXiv, Jan 19, 2020

This paper proposes a human-aware deblurring model that disentangles the motion blur between fore... more This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG). The proposed model is based on a triple-branch encoder-decoder architecture. The first two branches are learned for sharpening FG humans and BG details, respectively; while the third one produces global, harmonious results by comprehensively fusing multi-scale deblurring information from the two domains. The proposed model is further endowed with a supervised, human-aware attention mechanism in an end-toend fashion. It learns a soft mask that encodes FG human information and explicitly drives the FG/BG decoderbranches to focus on their specific domains. To further benefit the research towards Human-aware Image Deblurring, we introduce a large-scale dataset, named HIDE, which consists of 8,422 blurry and sharp image pairs with 65,784 densely annotated FG human bounding boxes. HIDE is specifically built to span a broad range of scenes, human object sizes, motion patterns, and background complexities. Extensive experiments on public benchmarks and our dataset demonstrate that our model performs favorably against the state-of-the-art motion deblurring methods, especially in capturing semantic details.

Jianbing Shen

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