Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Composite Spatial Reasoning

• URL: http://arxiv.org/abs/2410.16162v2
• Date: Thu, 21 Nov 2024 18:05:04 GMT
• Title: Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Composite Spatial Reasoning
• Authors: Yihong Tang, Ao Qu, Zhaokai Wang, Dingyi Zhuang, Zhaofeng Wu, Wei Ma, Shenhao Wang, Yunhan Zheng, Zhan Zhao, Jinhua Zhao,
• Abstract summary: Vision language models (VLMs) have demonstrated impressive performance across a wide range of downstream tasks. Our evaluation reveals that state-of-the-art VLMs frequently generate implausible and incorrect responses to composite spatial reasoning problems. To address this, we explore an effective approach to enhance 2D spatial reasoning within VLMs by training the model solely on basic spatial capabilities.
• Score: 19.399925987942204
• License:
• Abstract: Vision language models (VLMs) have demonstrated impressive performance across a wide range of downstream tasks. However, their proficiency in spatial reasoning remains limited, despite its crucial role in tasks involving navigation and interaction with physical environments. Specifically, most of these tasks rely on the core spatial reasoning capabilities in two-dimensional (2D) environments, and our evaluation reveals that state-of-the-art VLMs frequently generate implausible and incorrect responses to composite spatial reasoning problems, including simple pathfinding tasks that humans can solve effortlessly at a glance. To address this, we explore an effective approach to enhance 2D spatial reasoning within VLMs by training the model solely on basic spatial capabilities. We begin by disentangling the key components of 2D spatial reasoning: direction comprehension, distance estimation, and localization. Our central hypothesis is that mastering these basic spatial capabilities can significantly enhance a model's performance on composite spatial tasks requiring advanced spatial understanding and combinatorial problem-solving, with generalized improvements in visual-spatial tasks. To investigate this hypothesis, we introduce Sparkle, a framework that fine-tunes VLMs on these three basic spatial capabilities by synthetic data generation and targeted supervision to form an instruction dataset for each capability. Our experiments demonstrate that VLMs fine-tuned with Sparkle achieve significant performance gains, not only in the basic tasks themselves but also in generalizing to composite and out-of-distribution spatial reasoning tasks. These findings underscore the effectiveness of mastering basic spatial capabilities in enhancing composite spatial problem-solving, offering insights into systematic strategies for improving VLMs' spatial reasoning capabilities.

Related papers

• SpatialCoT: Advancing Spatial Reasoning through Coordinate Alignment and Chain-of-Thought for Embodied Task Planning [42.487500113839666]
  We propose a novel approach to bolster the spatial reasoning capabilities of Vision-Language Models (VLMs) Our approach comprises two stages: spatial coordinate bi-directional alignment, and chain-of-thought spatial grounding. We evaluate our method on challenging navigation and manipulation tasks, both in simulation and real-world settings.
  arXiv  Detail & Related papers  (2025-01-17T09:46:27Z)
• SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through Hierarchical Evaluation [7.659514491338669]
  Current vision-language models may grasp basic spatial cues but struggle with the multi-dimensional spatial reasoning necessary for human-like understanding and real-world applications. We develop SPHERE, a hierarchical evaluation framework supported by a new human-annotated dataset. Benchmark evaluation of state-of-the-art models reveals significant deficiencies, especially in reasoning about distance and proximity. These findings expose critical blind spots in existing models and underscore the need for more advanced spatial reasoning techniques.
  arXiv  Detail & Related papers  (2024-12-17T09:10:55Z)
• Structured Spatial Reasoning with Open Vocabulary Object Detectors [2.089191490381739]
  Reasoning about spatial relationships between objects is essential for many real-world robotic tasks. We introduce a structured probabilistic approach that integrates rich 3D geometric features with state-of-the-art open-vocabulary object detectors. The approach is evaluated and compared against zero-shot performance of the state-of-the-art Vision and Language Models (VLMs) on spatial reasoning tasks.
  arXiv  Detail & Related papers  (2024-10-09T19:37:01Z)
• REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models [67.55362046790512]
  Vision-language models lack the ability to correctly reason over spatial relationships. We develop the REVISION framework which improves spatial fidelity in vision-language models. Our results and findings indicate that utilizing rendering-based frameworks is an effective approach for developing spatially-aware models.
  arXiv  Detail & Related papers  (2024-08-05T04:51:46Z)
• VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs [102.36953558562436]
  Vision language models (VLMs) are an exciting emerging class of language models (LMs) One understudied capability inVLMs is visual spatial planning. Our study introduces a benchmark that evaluates the spatial planning capability in these models in general.
  arXiv  Detail & Related papers  (2024-07-02T00:24:01Z)
• SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models [70.01883340129204]
  spatial reasoning is a crucial component of both biological and artificial intelligence. We present a comprehensive study of the capability of current state-of-the-art large language models (LLMs) on spatial reasoning.
  arXiv  Detail & Related papers  (2024-06-07T01:06:34Z)
• SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models [68.13636352687257]
  We introduce Spatial Region GPT (SpatialRGPT) to enhance VLMs' spatial perception and reasoning capabilities. During inference, when provided with user-specified region proposals, SpatialRGPT can accurately perceive their relative directions and distances. Our results demonstrate that SpatialRGPT significantly enhances performance in spatial reasoning tasks, both with and without local region prompts.
  arXiv  Detail & Related papers  (2024-06-03T17:59:06Z)
• SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities [59.39858959066982]
  understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. We develop an automatic 3D spatial VQA data generation framework that scales up to 2 billion VQA examples on 10 million real-world images. By training a VLM on such data, we significantly enhance its ability on both qualitative and quantitative spatial VQA.
  arXiv  Detail & Related papers  (2024-01-22T18:01:01Z)
• Improving Vision-and-Language Reasoning via Spatial Relations Modeling [30.477235227733928]
  Visual commonsense reasoning (VCR) is a challenging multi-modal task. The proposed method can guide the representations to maintain more spatial context. We achieve the state-of-the-art results on VCR and two other vision-and-language reasoning tasks VQA, and NLVR.
  arXiv  Detail & Related papers  (2023-11-09T11:54:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.