Yangguang Li

Yangguang Li(李阳光)

I am working at SenseTime from 2019 to now. I obtained my master degree from Beijing University of Posts and Telecommunications and bachelor degree from Tianjin University.

Currently, I am focusing on the exploration and exploitation of autonomous driving camera perception models(Monocular and BEV). Before that I focused on general vision(multi-modal/big model) and Detection(face/human/traffic/structure/keypoint/video/automl) research.

Email / Linkedin / Zhihu / Github / Google Scholar

News

January 2023: One paper gets accepted to Pattern Recognition 2023.
October 2022: One paper gets accepted to NeurIPS ML4AD 2022.
October 2022: One paper gets accepted to EMNLP 2022.
September 2022: One paper gets accepted to BMVC 2022.
September 2022: One paper gets accepted to NeurIPS 2022.
August 2022: One paper gets accepted to COLING 2022.
July 2022: One paper gets accepted to ECCV 2022.
June 2022: 1st place in Embodied AI Challenge @ CVPR 2022.
June 2022: 2nd place in UG2+ Challenge @ CVPR 2022.
June 2022: One papers gets accepted to ICML workshops 2022.
April 2022: One papers gets accepted to CVPR workshops 2022.
April 2022: One paper gets accepted to IJCAI 2022 as long oral.
January 2022: One paper gets accepted to ICLR 2022.
April 2022: OpenGVLab is released.
November 2021: INTERN general vision technical report is released.

Publications

	Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies Xingrun Xing, Yangguang Li, Wei Li, Wenrui Ding, Yalong Jiang, Yufeng Wang, Jing Shao, Chunlei Liu, Xianglong Liu ECCV, 2022 PDF, bibtex, code We propose BCDNet, a newly designed method to binary neural modules, which enables BNNs to learn effective contextual dependencies.
	Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, Junjie Yan ICLR*, 2022 PDF, bibtex, code, video presentation We propose Data efficient CLIP (DeCLIP), a method to efficiently train CLIP via utilizing the widespread supervision among the image-text data.
	Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision Yufeng Cui, Lichen Zhao, Feng Liang, Yangguang Li^†, Jing Shao ICMLW*, 2022 PDF, bibtex, code We propose a CLIP benchmark of data, model, and supervision.
	RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training Luya Wang, Feng Liang, Yangguang Li, Honggang Zhang, Wanli Ouyang, Jing Shao IJCAI, 2022, Long oral PDF, bibtex We propose RePre to extends contrastive frameworks by adding a branch for reconstructing raw image pixels in parallel with the existing contrastive objective.
	Task-Balanced Distillation for Object Detection Ruining Tang, Zhenyu Liu, Yangguang Li, Yiguo Song, Hui Liu, Qide Wang, Jing Shao, Guifang Duan, Jianrong Tan Pattern Recognition, 2023 PDF, bibtex We alleviate the problem of inconsistent spatial distributions between classification score and localization quality (IOU) in detection task by designing a customized knowledge distillation teacher-student training workflow.
	1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022) Dong An, Zun Wang, Yangguang Li, Yi Wang, Yicong Hong, Yan Huang, Liang Wang, Jing Shao Winner of the 2nd RxR-Habitat Competition, 2022 PDF, bibtex We present a modular plan-and-control approach, which consists of three modules: the candidate waypoints predictor (CWP), the history enhanced planner and the tryout controller.
	IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification Hao Wang, Yangguang Li, Zhen Huang, Yong Dou COLING, 2022 PDF, bibtex, code We propose IMCI to integrate multi-view contextual information for fact extraction and verification.
	R2F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference Hao Wang, Yixin Cao^†, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao EMNLP, 2022 PDF, bibtex, code We establish a general solution, named Retrieval, Reading and Fusion (R2F) framework, and a new setting, by analyzing the main challenges of DocNLI: interpretability, long-range dependency, and cross-sentence inference.
	INTERN: A New Learning Paradigm Towards General Vision Jing Shao, Siyu Chen, Yangguang Li, Kun Wang, Zhenfei Yin, Yinan He, Jianing Teng, Qinghong Sun, Mengya Gao, Jihao Liu, Gengshi Huang, .etc CoRR*, 2021 PDF, bibtex, code, project we propose a new learning paradigm named INTERN, which introduces a continuous learning scheme, including a highly extensible upstream pretraining pipeline leveraging large-scale data and various supervisory signals, as well as a flexible downstream adaptation towards diversified tasks.