16 papers
EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards
Omkar Thawakar, Shravan Venkatraman, Ritesh Thawkar +5
Recent advances in large multimodal models (LMMs) have enabled impressive reasoning and perception abilities, yet most existing training pipelines still depend on human-curated dat…
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…
AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock
Umair Nawaz, Muhammad Zaigham Zaheer, Ufaq Khan +4
Crops, fisheries and livestock form the backbone of global food production, essential to feed the ever-growing global population. However, these sectors face considerable challenge…
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…
TAViS: Text-bridged Audio-Visual Segmentation with Foundation Models
Ziyang Luo, Nian Liu, Xuguang Yang +5
Audio-Visual Segmentation (AVS) faces a fundamental challenge of effectively aligning audio and visual modalities. While recent approaches leverage foundation models to address dat…
MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalities
Tooba Tehreem Sheikh, Jean Lahoud, Rao Muhammad Anwer +3
Traditional object detection models in medical imaging operate within a closed-set paradigm, limiting their ability to detect objects of novel labels. Open-vocabulary object detect…