Title: SpatialBoost: Enhancing Visual Representation through Language-Guided Reasoning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: image representations, spatial reasoning, multi-modal vision representation

Abstract: Despite the remarkable success of large-scale pre-trained image representation models (i.e., vision encoders) across various vision tasks, they often fail to learn 3D spatial relationships between objects and backgrounds in the real world, constraining their effectiveness in various downstream applications. We attribute this to the limited availability of large-scale 3D training data, which makes it difficult for current image representation learning approaches to learn spatial relationships. This motivates the need for learning paradigms that rely on strong supervision while requiring less data. To address this, we propose a novel learning framework that enhances the spatial awareness of existing pre-trained vision encoders by injecting dense 3D spatial knowledge expressed in linguistic forms.
To be specific, the core idea involves converting dense 3D spatial information from 2D images into linguistic expressions, which is then used to inject such spatial knowledge into vision encoders through a Large Language Model (LLM). To this end, we adopt a multi-turn Chain-of-Thought (CoT) reasoning process that progressively incorporates dense spatial knowledge and builds hierarchical spatial understanding. To validate effectiveness, we adapt SpatialBoost to state-of-the-art vision encoders such as DINOv3, and evaluate its performance gains on a wide range of benchmarks requiring both 3D perception and general vision abilities.