Anshul Shah

I am a Ph.D. student at Johns Hopkins University in the department of Computer Science. I am advised by Prof. Rama Chellappa working on problems in Computer Vision and Machine Learning. I am generously supported by Amazon Fellowship. 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.

I interned at Microsoft Mixed Reality with Harpreet Sawhney and Ben Lundell in Summer of 2021, 2022 and at Mitsubishi Electric Research Labs with Anoop Cherian in Summer of 2020. I am also actively collaborating with Rachel Reetzke, Ryan Roemmich and Rebecca Landa on early diagnosis of developmental disorders through pose based action recognition.

Email  /  CV  /  Google Scholar  /  Twitter  /  Github

profile photo

  • 02/2023: Work on behavior recognition for early diagnosis of Autism Spectrum Disorder will be presented at INSAR 2023
  • 12/2022: Paper accepted at TMLR
  • 10/2022: Named as an Amazon Fellow as a part of JHU + Amazon initiative for Interactive AI! [AI2AI,Amazon Science]
  • 09/2022: Paper accepted at WACV 2023
  • 09/2022: Paper accepted at BMVC 2022 and NeurIPS 2022
  • 09/2022: Presented a poster on the colaborative work on ASD at Department of Medicine-Whiting School of Engineering Research Retreat
  • 06/2022: Started 2nd Research Internship with Microsoft's Mixed Reality team
  • 04/2022: Selected as Highlighted Reviewer of ICLR 2022
  • 01/2022: Received Student Scholarship for AAAI-2022
  • 12/2021: Max-Margin Contrastive Learning is accepted to AAAI-2022
  • 06/2021: Started as a Research Intern with Microsoft's Mixed Reality team
  • 10/2021: Pose and Joint-Aware Action Recognition is accepted to WACV 2022


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 STEPs: Self-Supervised Key Step Extraction from Unlabeled Procedural Videos

Anshul Shah, Benjamin Lundell, Harpreet Sawhney, Rama Chellappa
Under Submission

We address the problem of extracting key steps from unlabeled procedural videos

HaLP 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
Under Submission

We hallucinate latent postives for learning skeleton encoders without labels

DiffAlign 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

MVA 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.

ObjAwareCrop 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

FeLMI 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

AnisotropicImageNet 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

MMCL 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
PoseAction 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
BringingAlive 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

AttentionVehicle 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

BlurDetection 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

Template Credits : Jon Barron