Beyond Semantics: Rediscovering Spatial Awareness in Vision-Language Models

• URL: http://arxiv.org/abs/2503.17349v1
• Date: Fri, 21 Mar 2025 17:51:14 GMT
• Title: Beyond Semantics: Rediscovering Spatial Awareness in Vision-Language Models
• Authors: Jianing Qi, Jiawei Liu, Hao Tang, Zhigang Zhu,
• Abstract summary: Vision-Language Models (VLMs) excel at identifying and describing objects but struggle with spatial reasoning.<n>Inspired by the dual-pathway (ventral-dorsal) model of human vision, we investigate why VLMs fail spatial tasks despite strong object recognition capabilities.
• Score: 10.792834356227118
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) excel at identifying and describing objects but struggle with spatial reasoning such as accurately understanding the relative positions of objects. Inspired by the dual-pathway (ventral-dorsal) model of human vision, we investigate why VLMs fail spatial tasks despite strong object recognition capabilities. Our interpretability-driven analysis reveals a critical underlying cause: vision embeddings in VLMs are treated primarily as semantic ``bag-of-tokens," overshadowing subtle yet crucial positional cues due to their disproportionately large embedding norms. We validate this insight through extensive diagnostic experiments, demonstrating minimal performance impact when token orders or fine-grained spatial details are removed. Guided by these findings, we propose simple, interpretable interventions, including normalizing vision embedding norms and extracting mid-layer spatially rich features, to restore spatial awareness. Empirical results on both our synthetic data and standard benchmarks demonstrate improved spatial reasoning capabilities, highlighting the value of interpretability-informed design choices. Our study not only uncovers fundamental limitations in current VLM architectures but also provides actionable insights for enhancing structured perception of visual scenes.

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