4 papers
MMRefine: Unveiling the Obstacles to Robust Refinement in Multimodal Large Language Models
Gio Paik, Geewook Kim, Jinbae Im
This paper introduces MMRefine, a MultiModal Refinement benchmark designed to evaluate the error refinement capabilities of Multimodal Large Language Models (MLLMs). As the emphasi…
How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?
Seongyun Lee, Geewook Kim, Jiyeon Kim +4
Vision-Language adaptation (VL adaptation) transforms Large Language Models (LLMs) into Large Vision-Language Models (LVLMs) for multimodal tasks, but this process often compromise…
Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation
Seongyun Lee, Seungone Kim, Sue Hyun Park +2
Assessing long-form responses generated by Vision-Language Models (VLMs) is challenging. It not only requires checking whether the VLM follows the given instruction but also verify…
SCOB: Universal Text Understanding via Character-wise Supervised Contrastive Learning with Online Text Rendering for Bridging Domain Gap
Daehee Kim, Yoonsik Kim, DongHyun Kim +3
Inspired by the great success of language model (LM)-based pre-training, recent studies in visual document understanding have explored LM-based pre-training methods for modeling te…