Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs

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 spatial awareness into V-LLMs. We discover optimal coordinate representations data-efficient instruction fine-tuning objectives and pseudo-data generation strategies that lead to improved spatial awareness in V-LLMs. Additionally our resulting model improves VQA across image and video domains reduces undesired hallucination and generates better contextual object descriptions. Experiments across 5 vision-language tasks involving 14 different datasets establish the clear performance improvements achieved by our proposed framework.