Komal Kumar — accepted AI-conference papers

2026

MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images

Ankan Deria, Komal Kumar, Adinath Madhavrao Dukre, Eran Segal, Salman Khan, Imran Razzak

CVPR 2026

Multimodal large language models (MLLMs) have rapidly advanced, yet their adoption in medicine remains limited by gaps in domain coverage, modality alignment, and grounded reasoning. In this work, we introduce MedMO, a medical foundation model built upon a generalized MLLM architecture and trained e

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2025

DEFT: Decompositional Efficient Fine-Tuning for Text-to-Image Models

Komal Kumar, Rao Muhammad Anwer, Fahad Shahbaz Khan, Salman Khan, Ivan Laptev, Hisham Cholakkal

NeurIPS 2025poster

Efficient fine-tuning of pre-trained Text-to-Image (T2I) models involves adjusting the model to suit a particular task or dataset while minimizing computational resources and limiting the number of trainable parameters. However, it often faces challenges in striking a trade-off between aligning with…

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