5 papers
BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning
Shaokai Ye, Vasileios Saveris, Yihao Qian +3
Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the era of multimodal large langu…
DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
Kartik Narayan, Yang Xu, Tian Cao +7
Multimodal Large Language Models (MLLMs) in real-world applications require access to external knowledge sources and must remain responsive to the dynamic and ever-changing real-wo…
MM-Spatial: Exploring 3D Spatial Understanding in Multimodal LLMs
Erik Daxberger, Nina Wenzel, David Griffiths +8
Multimodal large language models (MLLMs) excel at 2D visual understanding but remain limited in their ability to reason about 3D space. In this work, we leverage large-scale high-q…
FastVLM: Efficient Vision Encoding for Vision Language Models
Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li +8
Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popula…
MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs
Yusu Qian, Hanrong Ye, Jean-Philippe Fauconnier +3
We introduce MIA-Bench, a new benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to strictly adhere to complex instructions. Our benchmark com…