Vatsal Agarwal

I am a third year Ph.D student in the department of Computer Science at the University of Maryland (UMD), advised by Professor Abhinav Shrivastava. My research lies at the intersection of computer vision and deep learning.

I completed my undergrad in Computer Science at the University of Maryland in 2022. During my undergrad, I worked as a research assistant at the National Institutes of Health Clinical Center to develop deep learning models for medical imaging. I also interned as a machine learning engineer at DeepHealth.

In the past, I have been fortunate to have worked with Youbao Tang, Bill Lotter, Ronald Summers, Suhas Srinivasan.

Email / CV / Scholar / Github

Research

I am interested in developing computer vision models for multimodal understanding (e.g. MLLMs, open-vocabulary segmentation). I am also broadly interested in developing efficient attention methods that scale across various applications including image classification and human mesh recovery.

	Multimodal Understanding using Stable-Diffusion as a Task Aware Feature Extractor Vatsal Agarwal, Gefen Kohavi , Matthew Gwilliam , Eshan Verma , Daniel Ulbricht , Abhinav Shrivastava Under Review Paper Explore using text-to-image diffusion models as visual encoders for MLLMs.
	MAPS: Memory Augmented Panoptic Segmentation Vatsal Agarwal, Saksham Suri, Max Ehrlich, Abhinav Shrivastava Under Review Paper Explore potential of VLM-generated neural memory for panoptic segmentation.
	Do Text-free Diffusion Models Learn Discriminative Visual Representations? Matthew Gwilliam* , Soumik Mukhopadhyay, Vatsal Agarwal^✝, Yosuke Yamaguchi^✝, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou, Abhinav Shrivastava ECCV 2024* Project Page \|Paper Explore diffusion models as unified unsupervised image representation learning models for many recognition tasks. Propose DifFormer and DifFeed, novel mechanisms for fusing diffusion features for image classification.
	Diffusion Models Beat GANs on Image Classification Matthew Gwilliam* , Soumik Mukhopadhyay, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Tianyi Zhou, Abhinav Shrivastava preprint only* Project Page \|Paper Show the possible utility of diffusion models as unified unsupervised image representation learners.
	Coarse-to-Fine Human Mesh Recovery with Transformers Vatsal Agarwal, Mara Levy, Max Ehrlich, Youbao Tang, Ning Zhang, Abhinav Shrivastava ECCV 2024 Workshop: Towards a Complete Analysis of People: Fine-Grained Understanding for Real-World Applications Paper Build efficient Transformer design for non-parametric human mesh recovery with coarse-to-fine pipeline.
	Weakly Supervised Lesion Co-segmentation on CT Scans Vatsal Agarwal, Youbao Tang, Jing Xiao, Ronald M. Summers IEEE International Symposium on Biomedical Imaging (ISBI), 2020 Paper Conducted thorough investigation on impact of different attention mechanisms for lesion segmentation.
	Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation Vatsal Agarwal, Youbao Tang, Jing Xiao, Ronald M. Summers SPIE Medical Imaging, 2020 Paper Developed attention-based co-segmentation model and applied it to task of lesion segmentation.

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