Anshul Shah

I am a Research Scientist at Apple. I graduated with a Ph.D. in Computer Science from Johns Hopkins University in Feb'24 where I was advised by Prof. Rama Chellappa and was working on problems in Computer Vision and Machine Learning. I also obtained an MS in Computer Science from University of Maryland, College Park.

Previously I obtained a B.Tech(Honors) and M.Tech in Electrical Engineering from Indian Institute of Technology Madras. At IIT-M I was working under the guidance of Prof. A.N Rajagopalan in the area of Image Deblurring and Video Generation.

Email / CV / Google Scholar / Twitter / Github

News

03/2024: Started as a Research Scientist at Apple
02/2024: Successfully defended my PhD dissertation
10/2023: Invited Talk on STEPs at ICCV's CV4Metaverse workshop
10/2023: Proposal on SSL co-written with PI Rama Chellappa was awarded Amazon Research award (2023-24).
07/2023: Paper accepted at ICCV 2023
03/2023: Started Research Internship at Apple's ML Research org with Raviteja, Gierad, Anurag and Karren
10/2022: Named as an Amazon Fellow as a part of JHU + Amazon initiative for Interactive AI! [AI2AI,Amazon Science]

Research

I am interested in computer vision, machine learning. Specifically, my current research interests are in Self-Supervised learning, learning from complex videos and learning from multiple modalities and sensors.

	STEPs: Self-Supervised Key Step Extraction from Unlabeled Procedural Videos Anshul Shah, Benjamin Lundell, Harpreet Sawhney, Rama Chellappa ICCV 2023 We address the problem of extracting key steps from unlabeled procedural videos arXiv
	HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions Anshul Shah, Aniket Roy, Ketul Shah, Shlok Mishra, David Jacobs, Anoop Cherian, Rama Chellappa CVPR 2023 We hallucinate latent postives for learning skeleton encoders without labels
	DiffAlign : Few-shot learning using diffusion based synthesis and alignment Aniket Roy, Anshul Shah, Ketul Shah, Anirban Roy, Rama Chellappa Under Submission We leverage the recent success of the generative models for few-shot learning arXiv
	Multi-View Action Recognition using Contrastive Learning Ketul Shah, Anshul Shah, Chun Pong Lau, Celso M. de Melo, Rama Chellappa WACV 2023 A method for RGB-based action recognition using multi-view videos.
	Object-Aware Cropping for Self-Supervised Learning Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, Dilip Krishnan TMLR 2022 A novel cropping strategy for SSL on uncurated datasets arXiv
	FeLMi : Few shot Learning with hard Mixup Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, Rama Chellappa NeurIPS 2022 We propose hard mixup to improve few shot learning
	Learning Visual Representations for Transfer Learning by Suppressing Texture Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Jonghyun Choi, Abhinav Shrivastava, Abhishek Sharma, David Jacobs BMVC 2022 Anisotropic diffusion based augmentation to reduce texture bias in supervised and self-supervised approaches arXiv
	Max-Margin Contrastive Learning Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian AAAI 2022 We propose a novel objective for contrastive learning motivated by SVMs arXiv / video / code
	Pose and Joint-Aware Action Recognition Anshul Shah, Shlok Mishra, Ankan Bansal, Jun-Cheng Chen, Rama Chellappa, Abhinav Shrivastava WACV 2022 We present a new model and loss for Pose-based action recognition arXiv / video / code
	Bringing Alive Blurred Moments Kuldeep Purohit, Anshul Shah, A.N. Rajagopalan CVPR 2019 (Oral) We present a solution for the goal of extracting a video from a single motion blurred image arXiv
	Attention Driven Vehicle Re-identification and Unsupervised Anomaly Detection for Traffic Understanding Pirazh Khorramshahi, Neehar Peri, Amit Kumar, Anshul Shah, Rama Chellappa CVPRW 2019 Nvidia AI City Challenge An approach for anomaly detection and vehicle Re-ID on the challenging real-world dataset
	Learning Based Single Image Blur Detection and Segmentation Kuldeep Purohit, Anshul Shah, A.N. Rajagopalan ICIP 2018 A new solution to the problem of obtaining a blur-based segmentation map from a single image affected by motion or defocus blur

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