Ankit Singh
I am currently working as a Research engineer where my focus is to develop multi-modal large language models (LLMs). Previously, I was a research student at Computer Science & Engineering Department, Indian Institute of Technology, Madras ( IIT Madras ) ,
where I worked on Computer Vision and Deep Learning. I did my undergraduate studies in Computer Science at National Institute of Technology, Silchar( NIT Silchar )
Email  / 
Twitter  / 
LinkedIn  
|
|
Research
My research interests mainly lie in the areas of computer vision and deep learning.
In partiuclar, my current work is particularly focused on vision language models and label-efficient (Semi-Supervised/ Unsupervised /Self-Supervised ) approaches for deep-learning across Images/Videos.
In addition, I am also interested in video understanding, representation learning ,domain adaptation and transfer learning.
Google Scholar  / 
Github
|
Highlights
- Paper on permutation symmetries in Bayesian neural network posteriors at NeurIPS 2023
- Institute Research Award 2022, IIT Madras
- Student Volunteer for NeurIPS 2021
- Paper on Semi-Supervised Domain Adaptation accepted at NeurIPS 2021
- Paper on Semi-Supervised Action Recognition accepted at CVPR 2021.
- Paper on Mitigating data Imbalance accepted at ECCV-W 2020.
|
|
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation
Ankit Singh
Neural Information Processing Systems (NeurIPS), 2021
We propose a contrastive framework for semi-supervised domain adaptation (SSDA) where we use instance alignment between unlabeled target samples and centroid alignment between source and target domains.
|
|
Semi-Supervised Action Recognition with Temporal Contrastive Learning
Ankit Singh* , Omprakash Chakraborty*, Ashutosh Varshney, Rameswar Panda, Rogerio Feris, Kate Saenko, Abir Das
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
We propose a temporal contrastive learning framework for semi-supervised action recognition by using contrastive losses between different videos and groups of videos with similar actions.
|
|
Mitigating Dataset Imbalance via Joint Generation and Classification
Aadarsh Sahoo* , Ankit Singh* , Rameswar panda, Rogerio Feris, Abir Das
ECCV Workshop on Imbalance Problems in Computer Vision (ECCV-W), 2020
We introduce a joint dataset repairment strategy by combining classifier with a GAN that makes up for the deficit of training examples from the minority class by producing additional examples.
|
Services
- Reviewer: CVPR, ICCV, ECCV, ICLR, NeurIPS, AAAI, WACV, ACCV, BMVC, TPAMI
|
Website template from here
* denotes equal contribution
|
|