Jinwang Wang 王金旺

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Researcher at Alibaba DAMO Academy, Ph.D. from Wuhan University, advised by Prof. Wen Yang and Prof. Gui-Song Xia.

My research focuses on generative and multimodal foundation models, particularly vision generation and vision-language models, with an emphasis on visual generation, multimodal reasoning, and post-training.

Previously, I worked at Huawei on computer graphics and video generation models, and interned at SenseTime on object detection.

Selected Publications

Google Scholar citations: 4,172 Updated September 26, 2026

  1. yuan2026clinfusion.png
    ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding
    Hangjie Yuan, Yichen Qian, Zhiwei Tang, Xianzhe Xu, Lirong Wu, Sicheng Yang, Jinwang Wang, Pengju Wang, Zhitao Zeng, Yizeng Han, Yan Xing, Shengxuan Luo, Tao Feng, Qing Xie, Weigen Yao, Yi Yang, Zuozhu Liu, Jiasheng Tang, Shaocheng Wang, Jitao Wang, Jiahong Dong, Weihua Chen, Feng Xu, and Fan Wang
    arXiv, 2026.
    Unifies 2D and native 3D medical image understanding through a cascaded vision encoder, with clinically grounded evaluation of instruction following and report generation.
    Paper Code
  2. yang2026clinhallu.png
    ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning
    Sicheng Yang, Hangjie Yuan, Wenjun Zhang, Jinwang Wang, Yichen Qian, Weihua Chen, Fan Wang, and Lei Zhu
    EMNLP, 2026.
    Introduces a medical hallucination benchmark with 7,031 validated instances, tracing errors to visual recognition, knowledge recall, and reasoning integration.
    Paper Code
  3. liu2025unilumos.jpg
    UniLumos: Fast and Unified Image and Video Relighting with Physics-Plausible Feedback
    Pengwei Liu, Hangjie Yuan, Bo Dong, Jiazheng Xing, Jinwang Wang, Rui Zhao, Weihua Chen, and Fan Wang
    NeurIPS, 2025.
    Introduces a unified image and video relighting framework with physics-plausible feedback for fast, consistent, and controllable illumination editing.
    Paper Code
  4. wang2024weakly.jpg
    Weakly Supervised 3-D Building Reconstruction from Monocular Remote Sensing Images
    Weijia Li, Zhenghao Hu, Lingxuan Meng, Jinwang Wang, Juepeng Zheng, Runmin Dong, Conghui He, Gui-Song Xia, Haohuan Fu, and Dahua Lin
    TGRS, 2024.
    Develops a weakly supervised framework for reconstructing 3D buildings from monocular remote sensing imagery, reducing reliance on dense 3D annotations.
    Paper
  5. xu2023dynamic.jpg
    Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection
    Chang Xu, Jian Ding, Jinwang Wang, Wen Yang, Huai Yu, Lei Yu, and Gui-Song Xia
    CVPR, 2023.
    Proposes a dynamic coarse-to-fine learning strategy to improve oriented tiny object detection under extreme scale and localization uncertainty.
    Paper Code
  6. xu2022detecting.jpg
    Detecting Tiny Objects in Aerial Images: A Normalized Wasserstein Distance and a New Benchmark
    Chang Xu, Jinwang Wang, Wen Yang, Huai Yu, Lei Yu, and Gui-Song Xia
    ISPRS JPRS, 2022.
    Introduces the AI-TOD benchmark and Normalized Wasserstein Distance, establishing an evaluation and localization framework for aerial tiny object detection.
    Paper Code
  7. rfla.jpg
    RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection
    Chang Xu, Jinwang Wang, Wen Yang, Huai Yu, Lei Yu, and Gui-Song Xia
    ECCV, 2022.
    Proposes Gaussian receptive field based label assignment to better match anchors with tiny objects under weak and noisy spatial overlaps.
    Paper Code
  8. loft.jpg
    Learning to Extract Building Footprints from Off-Nadir Aerial Images
    Jinwang Wang, Lingxuan Meng, Weijia Li, Wen Yang, Lei Yu, and Gui-Song Xia
    TPAMI, 2022.
    Introduces an end-to-end framework for extracting building footprints from off-nadir aerial images by handling roof-footprint misalignment.
    Paper Code
  9. nwd.jpg
    A Normalized Gaussian Wasserstein Distance for Tiny Object Detection
    Jinwang Wang, Chang Xu, Wen Yang, and Lei Yu
    arXiv, 2021.
    Reformulates bounding-box similarity with Normalized Wasserstein Distance, addressing the scale-sensitive failure of IoU for tiny object detection.
    Paper Code
  10. aitod.jpg
    Tiny Object Detection in Aerial Images
    Jinwang Wang, Wen Yang, Haowen Guo, Ruixiang Zhang, and Gui-Song Xia
    ICPR, 2021.
    Defines aerial tiny object detection as a focused benchmark problem and introduces AI-TOD to expose failures of conventional detectors.
    Paper Code
  11. dotd.jpg
    Dot Distance for Tiny Object Detection in Aerial Images
    Chang Xu, Jinwang Wang, Wen Yang, and Lei Yu
    CVPRW, 2021.
    Proposes dot-distance based localization to represent and detect tiny objects more robustly when bounding boxes become unreliable.
    Paper
  12. iccv2021.jpg
    3D Building Reconstruction from Monocular Remote Sensing Images
    Weijia Li, Lingxuan Meng, Jinwang Wang, Conghui He, Gui-Song Xia, and Dahua Lin
    ICCV, 2021.
    Develops a monocular remote sensing approach for 3D building reconstruction, bridging 2D aerial imagery and structured urban geometry.
    Paper
  13. isprs2021.jpg
    ULSD: Unified Line Segment Detection Across Pinhole, Fisheye, and Spherical Cameras
    Hao Li, Huai Yu, Jinwang Wang, Wen Yang, Lei Yu, and Sebastian Scherer
    ISPRS JPRS, 2021. (The U. V. Helava Award – Best Paper 2021; Core idea contribution)
    Introduces a unified line segment detection framework that generalizes across pinhole, fisheye, and spherical camera models.
    Paper Code
  14. probmapobb.jpg
    Learning Center Probability Map for Detecting Objects in Aerial Images
    Jinwang Wang, Wen Yang, Heng-Chao Li, Haijian Zhang, and Gui-Song Xia
    TGRS, 2020.
    Proposes center probability maps for aerial object detection, improving localization by modeling center confidence in complex scenes.
    Paper Code
  15. wang2019mask.jpg
    Mask OBB: A Semantic Attention-Based Mask Oriented Bounding Box Representation for Multi-Category Object Detection in Aerial Images
    Jinwang Wang, Jian Ding, Haowen Guo, Wensheng Cheng, Ting Pan, and Wen Yang
    Remote Sens., 2019.
    Introduces a semantic attention based oriented bounding box representation for multi-category object detection in aerial images.
    Paper
  16. lin2019development.jpg
    Development of an Autonomous Unmanned Aerial Manipulator Based on a Real-Time Oriented-Object Detection Method
    Shijie Lin, Jinwang Wang, Rui Peng, and Wen Yang
    Sensors, 2019.
    Builds an autonomous unmanned aerial manipulator system powered by real-time oriented object detection for robotic aerial operation.
    Paper

Honors & Awards

  • Academic Innovation Second Prize Scholarship (Top 3%) — 2022
  • Yu Gang & Song Xiao Scholarship (Top 1%) — 2020
  • National Graduate Scholarship (Top 1%) — 2017, 2018
  • National First Prize (TOP5) & Best Paper Award, Graduate Electronic Design Competition — 2018
  • 6th Place & Finalist, ICRA & DJI RoboMaster AI/Robotics Challenge — 2017, 2018
  • National Second Prize, Graduate Electronic Design Competition — 2017
  • Chun-Tsung Scholar (Top 1%) — 2016
  • Provincial Grand Prize, National Undergraduate Electronics Design Contest — 2015
  • National Third Prize, Challenge Cup — 2015
  • National Second Prize, National Mathematical Modeling Competition — 2014
  • National Scholarship (Top 1%) — 2014
  • National Encouragement Scholarship (Top 1%) — 2013, 2015

Projects

Arxiv Daily Paper Daily arXiv paper tracking and analysis dashboard for computer vision, AI, language, and machine learning papers. Open project