Ankit Singh

I am a research student at Computer Science & Engineering Department, Indian Institute of Technology, Madras ( IIT Madras ) , where I work on Computer Vision and Deep Learning.
I did my undergraduate studies in Computer Science at National Institute of Technology, Silchar( NIT Silchar )

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Research

My research interests mainly lie in the areas of computer vision and deep learning. In partiuclar, my current work is particularly focused on 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.

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Highlights
  • Student Volunteer for NeurIPS 2021
  • Paper on Semi-Supervised Domain Adaptation accepted at NeurIPS 2021
  • Student Volunteer for ICCV 2021
  • Invited to be a reviewer of DNetCV Workshop, CVPR 2021
  • Paper on Semi-Supervised Action Recognition accepted at CVPR 2021.
  • Paper on Mitigating data Imbalance accepted at ECCV-W 2020.
Publications
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.


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* denotes equal contribution