Transformer-based Spatial Grounding: A Comprehensive Survey

2025-07-16

AI Summary

The spatial referring expression grounding (REG) field lacks a systematic survey of Transformer-based approaches, benchmark datasets, evaluation metrics, and industrial applicability. Method: This paper presents the first comprehensive review of Transformer-based spatial REG research from 2018 to 2025, covering model architectures (e.g., cross-modal attention, multimodal representation learning), mainstream benchmarks (RefCOCO series, G-Ref), and evaluation protocols (IoU, Acc@0.5). Through structured comparative analysis, it traces technical evolution—from single-stage alignment to hierarchical reasoning, and from reliance on synthetic data to generalization in real-world scenarios. Contribution/Results: The paper proposes an industrial deployment guideline with best practices, offering theoretical foundations and methodological insights for developing robust, interpretable, and production-ready vision-language alignment models. It bridges academic advances with practical engineering requirements, facilitating reproducible, scalable, and trustworthy spatial REG systems.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Spatial grounding, the process of associating natural language expressions with corresponding image regions, has rapidly advanced due to the introduction of transformer-based models, significantly enhancing multimodal representation and cross-modal alignment. Despite this progress, the field lacks a comprehensive synthesis of current methodologies, dataset usage, evaluation metrics, and industrial applicability. This paper presents a systematic literature review of transformer-based spatial grounding approaches from 2018 to 2025. Our analysis identifies dominant model architectures, prevalent datasets, and widely adopted evaluation metrics, alongside highlighting key methodological trends and best practices. This study provides essential insights and structured guidance for researchers and practitioners, facilitating the development of robust, reliable, and industry-ready transformer-based spatial grounding models.

Problem

• Survey transformer-based models for spatial grounding tasks
• Analyze methodologies, datasets, and metrics in spatial grounding
• Provide guidance for industry-ready spatial grounding models

Innovation

• Transformer-based models enhance multimodal representation
• Systematic review of spatial grounding methodologies
• Identifies dominant architectures and evaluation metrics