A bit about my research

My research interest mainly lies in computer vision and image analysis using machine learning techniques. Specifically, I develop algorithms for learning representations, understanding people, and segmenting images. Below is a selection of my publications, the details of which can be found on Google Scholar. ​​​
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  • L. Yuan, R. Qian, Y. Cui, B. Gong, F. Schroff, M.-H. Yang, H. Adam, T. Liu. Contextualized spatio-temporal contrastive learning with self-supervision. CVPR 2022. [arXiv]

  • J. Zheng, X. Shi, A. Gorban, J. Mao, Y. Song, C.R. Qi, T. Liu, V. Chari, A. Cornman, Y. Zhou, C. Li, D. Anguelov. Multi-modal 3D human pose estimation with 2D weak supervision in autonomous driving. CVPR Workshop on Autonomous Driving, 2022. [arXiv]
 
  • J. Zhuang, B. Gong, L. Yuan, Y. Cui, H. Adam, N.C. Dvornek, S. Tatikonda, J.S. Duncan, T. Liu. Surrogate gap minimization improves sharpness-aware training. ICLR 2022. [OpenReview][arXiv][Website]
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  • H. Zhou, A. Kadav, A. Shamsian, S. Geng, Farley Lai, L. Zhao, T. Liu, M. Kapadia, H.P. Graf. COMPOSER: Compositional learning of group activity in videos. arXiv:2112.05892, 2021. [arXiv]
 
  • R. Qian, Y. Li, L. Yuan, B. Gong, T. Liu, M. Brown, S. Belongie, M.-H., Yang, H. Adam, Y. Cui. Exploring temporal granularity in self-supervised video representation learning. arXiv:2112.04480, 2021. [arXiv]
   
  • L. Zhao, Y. Wang, J. Zhao, L. Yuan, F. Schroff, H. Adam, X. Peng, D.N. Metaxas, T. Liu. Learning view-disentangled human pose representation by contrastive cross-view mutual information maximization. CVPR 2021 (Oral). [arXiv]
  
  • L. Zhao, T. Liu, X. Peng, D.N. Metaxas. Maximum-entropy adversarial data augmentation for improved generalization and robustness. NeurIPS 2020. [arXiv][Code]
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