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Visual Spatial Reasoning

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arxiv 2205.00363 v3 pith:7QAJJB5C submitted 2022-04-30 cs.CL cs.AIcs.CV

Fangyu Liu , Guy Emerson , Nigel Collier This is my paper
classification cs.CLcs.AIcs.CV
keywords spatialhumanmodelsrelationsdatasetnaturalreasoningvisual
verification ladder T0 review T1 audit T2 compute T3 formal
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Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (such as: under, in front of, and facing). While using a seemingly simple annotation format, we show how the dataset includes challenging linguistic phenomena, such as varying reference frames. We demonstrate a large gap between human and model performance: the human ceiling is above 95%, while state-of-the-art models only achieve around 70%. We observe that VLMs' by-relation performances have little correlation with the number of training examples and the tested models are in general incapable of recognising relations concerning the orientations of objects.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DELTAVID: Enhancing Fine-Grained Spatiotemporal Perception with Cross-Video Differences

    cs.CV 2026-06 conditional novelty 6.0 of 10

    Rule-reward training on controllable cross-video differences (Grounding + MCQ) improves Video MLLM local spatiotemporal evidence localization and transfers to general video QA benchmarks.

  2. Enhancing Spatial Reasoning in Vision-Language Models via Chain-of-Thought Prompting and Reinforcement Learning

    cs.CV 2025-07 conditional novelty 4.0 of 10

    Scene-graph-based chain-of-thought prompting and GRPO training improve spatial reasoning accuracy in vision-language models, and GRPO degrades less than supervised fine-tuning when question wording is flipped.

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