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 visual person understanding and biological/natural image segmentation. Below is a selection of my publications, details of which can be found on Google Scholar. ​
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• L. Zhao, T. Liu, X. Peng, D.N. Metaxas. Maximum-entropy adversarial data augmentation for improved generalization and robustness. NeurIPS 2020. [arXiv][Code]

 

• J.J. Sun, J. Zhao, L.-C. Chen, F. Schroff, H. Adam, T. Liu. View-Invariant probabilistic embedding for human pose. ECCV 2020 (Spotlight). [arXiv][Code][Presentation][Short Presentation]

 

• B. Cheng, M.D. Collins, Y. Zhu, T. Liu, T.S. Huang, H. Adam, L.-C. Chen. Panoptic-DeepLab: A simple, strong, and fast baseline for bottom-up panoptic segmentation. CVPR 2020. [arXiv][Code]

 

• J.J. Sun, T. Liu, A.S. Cowen, F. Schroff, H. Adam, G. Prasad. EEV Dataset: Predicting expressions evoked by diverse videos. arXiv:2001.05488, 2020. [arXiv]

   

• B. Cheng, M.D. Collins, Y. Zhu, T. Liu, T.S. Huang, H. Adam, L.-C. Chen. Panoptic-DeepLab. ICCV 2019 Joint COCO and Mapillary Recognition Workshop (Winner of the Mapillary Vista Panoptic Segmentation Task, Best Paper and Most Innovative Award). [arXiv][Code]

 

• T.-J. Yang, M.D. Collins, Y. Zhu, J.-J. Hwang, T. Liu, X. Zhang, V. Sze, G. Papandreou, L.-C. Chen. DeeperLab: Single-shot image parser. arXiv:1902.05093, 2019. [arXiv]

 

• J.J. Sun, T. Liu, G. Prasad. GLA in MediaEval 2018 Emotional Impact of Movies Task. MediaEval 2018 Multimedia Benchmark Workshop (Winner of the Emotional Impact of Movies Task). [arXiv][Presentation]

 

• P. Nguyen*, T. Liu*, G. Prasad, B. Han. Weakly supervised action localization by sparse temporal pooling network. CVPR 2018. [arXiv][Code] (* Equal contribution)

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• T. Liu, M. Zhang, M. Javanmardi, N. Ramesh, T. Tasdizen. SSHMT: Semi-supervised hierarchical merge tree for electron microscopy image segmentation. ECCV 2016 (Spotlight).​ [arXiv][Presentation][Code]

 

• T. Liu, M. Seyedhosseini, T. Tasdizen. Image segmentation using hierarchical merge tree. IEEE Transactions on Image Processing, 25, pp. 4596--4607, 2016. [arXiv][Code]

 

• M. Javanmardi, M. Sajjadi, T. Liu, T. Tasdizen. Unsupervised total variational loss for semi-supervised learning of semantic segmentation. arXiv: 1605.01368, 2016. [arXiv]

 

• I. Arganda-Carreras, S.C. Turaga, D.R. Berger, D. Ciresan, A. Giusti, L.M. Gambardella, J. Schmidhuber, D. Laptev, S. Dwivedi, J.M. Buhmann, T. Liu, M. Seyedhosseini, T. Tasdizen, L. Kamentsky, R. Burget, V. Uher, X. Tan, C. Sun, T.D. Pham, E. Bas, M.G. Uzunbas, A. Cardona, J. Schindelin, H.S. Seung. Crowdsourcing the creation of image segmentation algorithms for connectomics. Frontiers in Neuroanatomy, 9:142, 2015. [Publisher]

 

• C. Jones, T. Liu, N.W. Cohan, M.H. Ellisman, T. Tasdizen. Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images. Journal of Neuroscience Methods, 246, pp. 13--21, 2015. [Publisher]

 

• T. Liu, C. Jones, M. Seyedhosseini, T. Tasdizen. A modular approach to 3D electron microscopy image segmentation. Journal of Neuroscience Methods, 226, 88--102, 2014. [PDF][Code]

 

• T. Tasdizen, M. Seyedhosseini, T. Liu, C. Jones, E. Jurrus. Image segmentation for connectomics using machine learning. Computational Intelligence in Biomedical Imaging, pp. 237--278, Springer New York, 2014. [Publisher]

 

• T. Liu, M. Seyedhosseini, M.H. Ellisman, T. Tasdizen. Watershed merge forest classification for electron microscopy image stack segmentation. ICIP 2013. [PDF]

 

• C. Jones, T. Liu, M.H. Ellisman, T. Tasdizen. Semi-automatic neuron segmentation in electron microscopy images via sparse labeling. ISBI 2013. [PDF]

 

• T. Liu, E. Jurrus, M. Seyedhosseini, M.H. Ellisman, T. Tasdizen. Watershed merge tree classification for electron microscopy image segmentation. ICPR 2012. [PDF][Code]