Shaunak Halbe

ML PhD student
Georgia Institute of Technology

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About

I am a third year Machine Learning PhD student at Georgia Tech advised by Prof. Zsolt Kira. My research interests lie at the intersection of computer vision and natural language processing.

I am currently working on vision-and-language pretraining, (egocentric) video understanding and domain adaptation.

At Georgia Tech, I have been working on building vision-and-language models capable of understanding the open-world. Previously, I have worked on domain generalization and robustness at USC/Meta AI and on embodied visual navigation at CMU.

Research

	Grounding Descriptions in Images informs Zero-shot Visual Recognition Shaunak Halbe^* et. al. Under Review preprint We propose a new pretraining strategy for CLIP to learn fine-grained visual representations that exhibit strong zero-shot transfer performance.
	Continual Adaptation of Vision Transformers for Federated Learning Shaunak Halbe^, James Smith, Junjiao Tian, Zsolt Kira Transactions on Machine Learning Research (TMLR) 2024* Short Version: FL@FM Workshop, NeurIPS 2023 (Oral) paper / talk We propose a novel prompt learning and aggregation scheme for distributed training of foundation models
	Open-World Dialogue Driven Object Navigation Conference on Robot Learning (CoRL) 2023 (Demo Track) Coming Soon We demonstrate robot navigation to an open-set of objects described in natural language
	Robustness through Data Augmentation Loss Consistency Tianjian Huang^, Shaunak Halbe^, Chinnadhurai Sankar, Pooyan Amini, Satwik Kottur, Alborz Geramifard, Meisam Razaviyayn, Ahmad Beirami Transactions on Machine Learning Research (TMLR) 2022 paper We introduce a novel loss-level regularizer to improve robustness to spurious correlations in generative models
	A Closer Look at Rehearsal-Free Continual Learning James Smith, Junjiao Tian, Shaunak Halbe, Yen-Chang Hsu, Zsolt Kira CVPR-W 2023 paper We introduce knowledge distillation and regularization baselines using Foundation Models for rehearsal-free continual learning
	Reason & Act : A Modular Approach to Explanation Driven Agents for Vision and Language Navigation Shaunak Halbe, Ingrid Navarro, Jean Oh CMU Robotics Institute Working Papers Journal paper / poster / talk We present a modular agent for navigation with improved cross-modal grounding and semantic reasoning.
	Exploring Weaknesses of VQA Models through Attribution Driven Insights Shaunak Halbe ACL-W 2020 Short Version: CVPR-W 2020 paper / talk We present a consistency analysis of VQA models through the lens of attribution to evaluate adversarial robustness.