no code implementations • 27 Nov 2023 • Yuxiang Guo, Anshul Shah, Jiang Liu, Ayush Gupta, Rama Chellappa, Cheng Peng
Gait recognition holds the promise to robustly identify subjects based on walking patterns instead of appearance information.
no code implementations • 16 Nov 2023 • Aniket Roy, Maiterya Suin, Anshul Shah, Ketul Shah, Jiang Liu, Rama Chellappa
Diffusion models have advanced generative AI significantly in terms of editing and creating naturalistic images.
no code implementations • 4 Oct 2023 • Guoyizhe Wei, Feng Wang, Anshul Shah, Rama Chellappa
Federated learning is a distributed machine learning paradigm that allows multiple clients to collaboratively train a shared model with their local data.
1 code implementation • CVPR 2023 • Anshul Shah, Aniket Roy, Ketul Shah, Shlok Kumar Mishra, David Jacobs, Anoop Cherian, Rama Chellappa
In this work, we propose a new contrastive learning approach to train models for skeleton-based action recognition without labels.
1 code implementation • IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 • Ketul Shah, Anshul Shah, Chun Pong Lau, Celso M. de Melo, Rama Chellappa
We present a supervised contrastive learning framework to learn a feature embedding robust to changes in viewpoint, by effectively leveraging multi-view data.
Ranked #12 on Action Recognition on NTU RGB+D 120
1 code implementation • ICCV 2023 • Anshul Shah, Benjamin Lundell, Harpreet Sawhney, Rama Chellappa
We address the problem of extracting key steps from unlabeled procedural videos, motivated by the potential of Augmented Reality (AR) headsets to revolutionize job training and performance.
no code implementations • 11 Dec 2022 • Aniket Roy, Anshul Shah, Ketul Shah, Anirban Roy, Rama Chellappa
We generate captions from the limited training images and using these captions edit the training images using an image-to-image stable diffusion model to generate semantically meaningful augmentations.
no code implementations • 28 Jan 2022 • Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan
This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.
1 code implementation • 21 Dec 2021 • Anshul Shah, Suvrit Sra, Rama Chellappa, Anoop Cherian
Standard contrastive learning approaches usually require a large number of negatives for effective unsupervised learning and often exhibit slow convergence.
Ranked #108 on Self-Supervised Image Classification on <h2>oi</h2>
1 code implementation • 1 Dec 2021 • Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Janit Anjaria, Abhishek Sharma, David Jacobs, Dilip Krishnan
This assumption is mostly satisfied in datasets such as ImageNet where there is a large, centered object, which is highly likely to be present in random crops of the full image.
1 code implementation • 3 Nov 2020 • Shlok Mishra, Anshul Shah, Ankan Bansal, Janit Anjaria, Jonghyun Choi, Abhinav Shrivastava, Abhishek Sharma, David Jacobs
Recent literature has shown that features obtained from supervised training of CNNs may over-emphasize texture rather than encoding high-level information.
Ranked #19 on Object Detection on PASCAL VOC 2007
1 code implementation • 16 Oct 2020 • Anshul Shah, Shlok Mishra, Ankan Bansal, Jun-Cheng Chen, Rama Chellappa, Abhinav Shrivastava
Unlike other modalities, constellation of joints and their motion generate models with succinct human motion information for activity recognition.
Ranked #1 on Action Recognition on Mimetics
1 code implementation • CVPR 2019 • Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan
This network extracts embedded motion information from the blurred image to generate a sharp video in conjunction with the trained recurrent video decoder.
Ranked #37 on Image Deblurring on GoPro (using extra training data)