39 citations · 74 across the 11 of their papers we have counts for
12 papers
Omni-Embed-Mini: Binding Modalities Without Forgetting via Dense Distillation
Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Sahal Shaji Mullappilly +2
Extending a text embedding model to new modalities typically degrades text retrieval quality, and existing omni-modal embedders compensate with multi-billion parameters. We present…
MAviS: A Multimodal Conversational Assistant For Avian Species
Yevheniia Kryklyvets, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly +5
Fine-grained understanding and species-specific multimodal question answering are vital for advancing biodiversity conservation and ecological monitoring. However, existing multimo…
MediX-R1: Open Ended Medical Reinforcement Learning
Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Omair Mohamed +5
We introduce MediX-R1, an open-ended Reinforcement Learning (RL) framework for medical multimodal large language models (MLLMs) that enables clinically grounded, free-form answers…
A Benchmark for Omni-Modal Reasoning in Long Videos
Mohammed Irfan Kurpath, Jaseel Muhammad Kaithakkodan, Jinxing Zhou +12
Long-form omni-modal video understanding requires integrating vision, speech, and ambient audio with coherent long-context reasoning. Existing video benchmarks often trade off temp…
LLMVoX: Autoregressive Streaming Text-to-Speech Model for Any LLM
Sambal Shikhar, Mohammed Irfan Kurpath, Sahal Shaji Mullappilly +5
Recent advancements in speech-to-speech dialogue systems leverage LLMs for multimodal interactions, yet they remain hindered by fine-tuning requirements, high computational overhea…
BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities
Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Sara Pieri +8
We introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. It enables multi-turn c…
Semi-supervised Open-World Object Detection
Sahal Shaji Mullappilly, Abhishek Singh Gehlot, Rao Muhammad Anwer +2
Conventional open-world object detection (OWOD) problem setting first distinguishes known and unknown classes and then later incrementally learns the unknown objects when introduce…
BiMediX: Bilingual Medical Mixture of Experts LLM
Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan +4
In this paper, we introduce BiMediX, the first bilingual medical mixture of experts LLM designed for seamless interaction in both English and Arabic. Our model facilitates a wide r…
Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM
Sahal Shaji Mullappilly, Abdelrahman Shaker, Omkar Thawakar +4
Climate change is one of the most significant challenges we face together as a society. Creating awareness and educating policy makers the wide-ranging impact of climate change is…
XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models
Omkar Thawakar, Abdelrahman Shaker, Sahal Shaji Mullappilly +5
The latest breakthroughs in large vision-language models, such as Bard and GPT-4, have showcased extraordinary abilities in performing a wide range of tasks. Such models are traine…
An Empirical Study Of Self-supervised Learning Approaches For Object Detection With Transformers
Gokul Karthik Kumar, Sahal Shaji Mullappilly, Abhishek Singh Gehlot
Self-supervised learning (SSL) methods such as masked language modeling have shown massive performance gains by pretraining transformer models for a variety of natural language pro…
MuCoT: Multilingual Contrastive Training for Question-Answering in Low-resource Languages
Gokul Karthik Kumar, Abhishek Singh Gehlot, Sahal Shaji Mullappilly +1
Accuracy of English-language Question Answering (QA) systems has improved significantly in recent years with the advent of Transformer-based models (e.g., BERT). These models are p…