Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs

arXiv:2404.07449v1 Announce Type: new
Abstract: Integration of Large Language Models (LLMs) into visual domain tasks, resulting in visual-LLMs (V-LLMs), has enabled exceptional performance in vision-language tasks, particularly for visual question answering (VQA). However, existing V-LLMs (e.g. BLIP-2, LLaVA) demonstrate weak spatial reasoning and localization awareness. Despite generating highly descriptive and elaborate textual answers, these models fail at simple tasks like distinguishing a left vs right location. In this work, we explore how image-space coordinate based instruction fine-tuning objectives could inject …

abstract arxiv blip-2 cs.cv domain however integration language language models large language large language models llava llms localization objects performance question question answering reasoning spatial tasks type vision visual vqa