Papers by Sanjoy Chowdhury
ASPIRE: Language-Guided Data Augmentation for Improving Robustness Against Spurious Correlations (2024.findings-acl)
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Sreyan Ghosh, Chandra Kiran Evuru, Sonal Kumar, Utkarsh Tyagi, S Sakshi, Sanjoy Chowdhury, Dinesh Manocha
| Challenge: | Neural image classifiers often rely on non-predictive features that are spuriously correlated with the class labels in training data. |
| Approach: | They propose a language-guided data augmented with images without spurious correlations that can be used to augment training datasets for robust learning. |
| Outcome: | The proposed model improves the worst-group classification accuracy of prior methods by 1% - 38%. |
Can LLM’s Generate Human-Like Wayfinding Instructions? Towards Platform-Agnostic Embodied Instruction Synthesis (2024.naacl-short)
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| Challenge: | 83.3% of users find the synthesized instructions accurately capture the details of the environment and show characteristics similar to those of human-generated instructions. |
| Approach: | They propose an algorithm that uses in-context learning to condition an LLM to generate instructions using just a few references. |
| Outcome: | The proposed algorithm is platform-agnostic and 83.3% of users find it to be accurate and similar to human-generated instructions. |
APoLLo : Unified Adapter and Prompt Learning for Vision Language Models (2023.emnlp-main)
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| Challenge: | APoLLo improves generalization capabilities of vision-language pretrained models . despite being largely successful in terms of generalization, these models are difficult to fine-tune for few-shot learning-based downstream tasks. |
| Approach: | They propose a multi-modal approach that combines Adapter and Prompt learning for Vision-Language models. |
| Outcome: | The proposed approach improves generalization capabilities of vision-language pretrained models . it achieves a relative gain of 6.03% over MaPLe on 10 diverse datasets . |