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I am an Assistant Professor in the Department of Computer Science at the University of North Carolina at Charlotte. At UNC Charlotte, I am working on Video Representation Learning, and Robotic Vision. I am a member of the AI4Health Center and one of the founding members of the Charlotte Machine Learning Lab (CharMLab) at UNC Charlotte. Before this, I was a Postdoctoral Associate at Stony Brook University under the supervision of Michael S. Ryoo. In 2020, I completed my Ph.D. in Computer Science at INRIA, Sophia Antipolis, France under the supervision of Francois Bremond and Monique Thonnat. My Ph.D. thesis is on ¨Spatio-temporal attention mechanisms for Action Recognition¨ and click here to watch my Defense Presentation. I did my Post-Grad in Computer Science from the National Institute of Technology (NIT), Rourkela. |
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My research focuses on representation learning in long videos, leveraging vision-language models, multimodal inputs, and both egocentric and exocentric viewpoints. I am also interested in vision-language-action models for robot learning and the use of diffusion models for video generation. |
2025
2024
2023
2022
- Dominick Reilly
- Arkaprava Sinha
- Manish Kumar Govind (co-supervised with Prof. Pu Wang)
- Weston Bondurant (co-supervised with Prof. Stephanie Schuckers)
- Wenhao Chi
- Nitin Chandrasekhar (UG student at UNC Charlotte)
- Drew O’Donnell (UG student at UNC Charlotte)
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Dominick Reilly, Manish Kumar Govind, Le Xue, and Srijan Das. Preprint arXiv / Code We leverage the complementary nature of egocentric views to enhance LVLM’s understanding of exocentric ADL videos through online ego2exo distillation. |
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Arkaprava Sinha, Monish Soundar Raj, Pu Wang, Ahmed Helmy, and Srijan Das. Preprint arXiv / code MS-Temba is the first Mamba based architecture for action detection in long untrimmed videos that can be trained/tested on NVIDIA Jetson Nano. |
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Saarthak Kapse, Pushpak Pati, Srikar Yellapragada, Srijan Das , Rajarsi R. Gupta, Joel Saltz, Dimitris Samaras, Prateek Prasanna. To Appear in ICCV 2025 arXiv / Code Gigapixel Vision-Concept Knowledge Contrastive pretraining (GECKO) aligns WSIs with a Concept Prior for delivering clinically meaningful interpretability. |
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Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Mayur Jagdishbhai Patel, Hongfei Xue, Ahmed Helmy, Srijan Das, Pu Wang. To Appear in ICCV 2025 arXiv / Website A novel generative masked model for hand mesh recovery that synthesizes plausible 3D hand meshes. |
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Dominick Reilly, Rajatsubhra Chakraborty, Arkaprava Sinha, Manish Kumar Govind, Pu Wang, Francois Bremond, Le Xue, Srijan Das. CVPR 2025 arXiv / website / code LLAVIDAL, a Large Language Vision Model, incorporates 3D poses and relevant object trajectories to understand the intricate spatiotemporal relationships within ADLs. |
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Arkaprava Sinha, Dominick Reilly, Francois Bremond, Pu Wang, and Srijan Das. AAAI 2025 arXiv / Code Ski-models introduce 3D skeletons into the vision-language embedding space to enable effective zeroshot learning for ADL. |
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Muhammad Usama Saleem , Ekkasit Pinyoanuntapong, Pu Wang, Hongfei Xue, Srijan Das, Chen Chen. AAAI 2025 arXiv / Website A generative framework that reformulates monocular HMR as an image-conditioned generative task. |
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Wenhan Wu, Ce Zheng, Zihao Yang, Chen Chen, Srijan Das, Aidong Lu. ACM MM 2024 arXiv / code A frequency-aware attention module to unweave skeleton frequency representations for action recognition. |
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Prantik Howlader, Srijan Das, Hieu Le, Dimitris Samaras. ECCV 2024 arXiv / code A novel plug-in module designed for existing semi-supervised segmentation frameworks that offers patch-level supervision. |
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Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Pu Wang, Minwoo Lee, Srijan Das, Chen Chen. ECCV 2024 arXiv / website / code A novel text-to-motion generation framework. BAMM captures rich and bidirectional dependencies among motion tokens. |
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Dominick Reilly and Srijan Das. CVPR 2024 arXiv / code We introduce the first Pose Induced Video Transformer: PI-ViT (or π-ViT), a novel approach that augments the RGB representations learned by video transformers with 2D and 3D pose information. |
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Saarthak Kapse*, Pushpak Pati*, Srijan Das, Jingwei Zhang, Chao Chen, Maria Vakalopoulou, Joel Saltz, Dimitris Samaras, Rajarsi Gupta, Prateek Prasanna. CVPR 2024 arXiv / code Self-Interpretable MIL (SI-MIL), the first interpretable-by-design MIL method for gigapixel WSIs, which provides de novo feature-level interpretations grounded on pathological insights for a WSI. |
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Aritra Dutta, Srijan Das , Jacob Nielsen, Rajatsubhra Chakraborty, Mubarak Shah. CVPR 2024 arXiv / Website We present MAVREC, a video dataset where we record synchronized scenes from different perspectives -- ground camera and drone-mounted camera. |
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Saarthak Kapse, Srijan Das, Jingwei Zhang, Rajarsi R. Gupta, Joel Saltz, Dimitris Samaras, Prateek Prasanna. Medical Image Analysis (IF 10.9) arXiv A diversity-inducing pretraining technique, tailored to enhance representation learning in digital pathology. |
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Srijan Das, Tanmay Jain, Dominick Reilly, Pranav Balaji, Soumyajit Karmakar, Shyam Marjit, Xiang Li, Abhijit Das, and Michael S. Ryoo. WACV 2024 arXiv / code / Poster / Video This paper shows that jointly optimizing ViTs for the primary task and a Self-Supervised Auxiliary Task is surprisingly beneficial when the amount of training data is limited. |
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Rui Dai, Srijan Das, Michael S. Ryoo, Francois Bremond. BMVC 2023 arXiv / video This paper explains how to utilize OpenAI's CLIP for long-term action detection in videos. |
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Srijan Das and Michael S. Ryoo. 18th International Conference on Machine Vision Applications , July 2023 arXiv / Poster / Best Poster Award This paper focuses on designing video augmentation for self-supervised learning, we propose CMMC to make use of other modalities in videos for data mixing. |
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Srijan Das, and Michael S. Ryoo. WACV 2023 arXiv A framework for learning self-supervised video representation that is invariant to unseen camera viewpoints. |
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Xiang Li, Jinghuan Shang, Srijan Das, Michael S. Ryoo. NeurIPS 2022 arXiv / code The impacts of the existing self-supervised losses with Joint Learning framework for RL is limited, while there is no golden method that can dominate all tasks. |
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Jinghuan Shang, Srijan Das, Michael S. Ryoo. NeurIPS 2022 arXiv / Project Page / code 3DTRL is a light-weighted, plug-and play layer that recovers 3D information of visual tokens and leverages it for learning viewpoint-agnostic representations. |
 
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Rui Dai, Srijan Das, Saurav Sharma, Luca Minciullo, Lorenzo Garattoni, François Brémond, Gianpiero Francesca. T-PAMI 2022 Project Link / Code TSU is a new untrimmed daily-living dataset consisting of 51 activities performed in a spontaneous manner, captured from non-optimal viewpoints. |
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Rui Dai, Srijan Das, Kumara Kahatapitiya, Michael S. Ryoo, Francois Bremond. CVPR 2022 arXiv / code A ConvTransformer network that explores global and local temporal relations at multiple resolutions. |
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Srijan Das, Rui Dai, Di Yang, Francois Bremond, TPAMI, 2021 arXiv / code VPN++ is an extension of our VPN model (ECCV 2020). VPN++ hallucinates pose driven features while not requiring costly 3D Poses at inference. |
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Rui Dai, Srijan Das, Francois Bremond. BMVC 2021, Oral |
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Rui Dai, Srijan Das, Francois Bremond. ICCV 2021 |
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Rui Dai, Srijan Das, Luca Minciullo, Lorenzo Garattoni, Gianpiero Francesca and Francois Bremond. WACV 2021 Code / Video / Poster |
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Srijan Das, Saurav Sharma, Rui Dai, Francois Bremond, Monique Thonnat. ECCV 2020 Code |
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Srijan Das, Monique Tonnat and Francois Bremond. WACV 2020 |
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Srijan Das, Rui Dai, Michal Koperski, Luca Minciullo, Lorenzo Garattoni, Francois Bremond and Gianpiero Francesca. ICCV 2019 Project Link / Code |
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Srijan Das, Arpit Chaudhary, Francois Bremond and Monique Thonnat. WACV 2019 |
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