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20232026
most citedPromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks

1 citations · 1 across the 12 of their papers we have counts for

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cs.CL2024★ 1 cited

PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks

Soumya Suvra Ghosal, Soumyabrata Pal, Koyel Mukherjee +1

Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL). However, ICL performance is highly dependent on…

cs.CV2024

Meerkat: Audio-Visual Large Language Model for Grounding in Space and Time

Sanjoy Chowdhury, Sayan Nag, Subhrajyoti Dasgupta +4

Leveraging Large Language Models' remarkable proficiency in text-based tasks, recent works on Multi-modal LLMs (MLLMs) extend them to other modalities like vision and audio. Howeve…

cs.CV2024

IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning

Soumya Suvra Ghosal, Samyadeep Basu, Soheil Feizi +1

Image-text contrastive models such as CLIP learn transferable and robust representations for zero-shot transfer to a variety of downstream tasks. However, to obtain strong downstre…

cs.CV2024

MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models

Sanjoy Chowdhury, Sayan Nag, K J Joseph +2

Music is a universal language that can communicate emotions and feelings. It forms an essential part of the whole spectrum of creative media, ranging from movies to social media po…

cs.LG2024

EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes

Ruichen Wang, Dinesh Manocha

We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation that is used for wireless communication simulation in 3D indoor environ…

cs.RO2024

Can LLMs Generate Human-Like Wayfinding Instructions? Towards Platform-Agnostic Embodied Instruction Synthesis

Vishnu Sashank Dorbala, Sanjoy Chowdhury, Dinesh Manocha

We present a novel approach to automatically synthesize "wayfinding instructions" for an embodied robot agent. In contrast to prior approaches that are heavily reliant on human-ann…