Spatial-ViLT: Enhancing Visual Spatial Reasoning through Multi-Task Learning

• URL: http://arxiv.org/abs/2510.03441v1
• Date: Fri, 03 Oct 2025 19:04:15 GMT
• Title: Spatial-ViLT: Enhancing Visual Spatial Reasoning through Multi-Task Learning
• Authors: Chashi Mahiul Islam, Oteo Mamo, Samuel Jacob Chacko, Xiuwen Liu, Weikuan Yu,
• Abstract summary: Vision-language models (VLMs) have advanced multimodal reasoning but still face challenges in spatial reasoning for 3D scenes and complex object configurations.<n>We introduce SpatialViLT, an enhanced VLM that integrates spatial features like depth maps, 3D coordinates, and edge maps through a multi-task learning framework.<n>We propose two variants: SpatialViLT and MaskedSpatialViLT, focusing on full and masked object regions, respectively.<n>Our models excel in spatial reasoning categories such as directional, topological, and proximity relations, as demonstrated on the challenging Visual Spatial Reasoning (VSR) dataset.
• Score: 1.5604334108839177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) have advanced multimodal reasoning but still face challenges in spatial reasoning for 3D scenes and complex object configurations. To address this, we introduce SpatialViLT, an enhanced VLM that integrates spatial features like depth maps, 3D coordinates, and edge maps through a multi-task learning framework. This approach enriches multimodal embeddings with spatial understanding. We propose two variants: SpatialViLT and MaskedSpatialViLT, focusing on full and masked object regions, respectively. Additionally, SpatialEnsemble combines both approaches, achieving state-of-the-art accuracy. Our models excel in spatial reasoning categories such as directional, topological, and proximity relations, as demonstrated on the challenging Visual Spatial Reasoning (VSR) dataset. This work represents a significant step in enhancing the spatial intelligence of AI systems, crucial for advanced multimodal understanding and real-world applications.

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