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Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agents

Joongwon Chae, Zhenyu Wang, Lian Zhang, Dongmei Yu, Peiwu Qin

TL;DR

Multimodal vision-language models struggle with precise spatial localization. The authors propose a simple grid-based visual position encoding by overlaying a $9\times9$ grid onto input images, enabling explicit spatial grounding without backbone changes. On COCO 2017, the approach yields substantial localization gains, with $IoU$ rising from $0.27$ to $0.56$ and $GIoU$ from $0.18$ to $0.53$, supported by attention-grounding visualizations. The method is lightweight and practical for applications requiring spatial reasoning, such as robotics, medical imaging, and autonomous navigation.

Abstract

Recent advances in multimodal models have demonstrated impressive capabilities in object recognition and scene understanding. However, these models often struggle with precise spatial localization - a critical capability for real-world applications. Inspired by how humans use grid-based references like chess boards and maps, we propose introducing explicit visual position encoding through a simple grid overlay approach. By adding a 9x9 black grid pattern onto input images, our method provides visual spatial guidance analogous to how positional encoding works in transformers, but in an explicit, visual form. Experiments on the COCO 2017 dataset demonstrate that our grid-based approach achieves significant improvements in localization accuracy, with a 107.4% increase in IoU (from 0.27 to 0.56) and a 194.4% improvement in GIoU (from 0.18 to 0.53) compared to baseline performance. Through attention visualization analysis, we show how this visual position encoding helps models better ground spatial relationships. Our method's simplicity and effectiveness make it particularly valuable for applications requiring accurate spatial reasoning, such as robotic manipulation, medical imaging, and autonomous navigation.

Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agents

TL;DR

Multimodal vision-language models struggle with precise spatial localization. The authors propose a simple grid-based visual position encoding by overlaying a grid onto input images, enabling explicit spatial grounding without backbone changes. On COCO 2017, the approach yields substantial localization gains, with rising from to and from to , supported by attention-grounding visualizations. The method is lightweight and practical for applications requiring spatial reasoning, such as robotics, medical imaging, and autonomous navigation.

Abstract

Recent advances in multimodal models have demonstrated impressive capabilities in object recognition and scene understanding. However, these models often struggle with precise spatial localization - a critical capability for real-world applications. Inspired by how humans use grid-based references like chess boards and maps, we propose introducing explicit visual position encoding through a simple grid overlay approach. By adding a 9x9 black grid pattern onto input images, our method provides visual spatial guidance analogous to how positional encoding works in transformers, but in an explicit, visual form. Experiments on the COCO 2017 dataset demonstrate that our grid-based approach achieves significant improvements in localization accuracy, with a 107.4% increase in IoU (from 0.27 to 0.56) and a 194.4% improvement in GIoU (from 0.18 to 0.53) compared to baseline performance. Through attention visualization analysis, we show how this visual position encoding helps models better ground spatial relationships. Our method's simplicity and effectiveness make it particularly valuable for applications requiring accurate spatial reasoning, such as robotic manipulation, medical imaging, and autonomous navigation.

Paper Structure

This paper contains 31 sections, 11 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Position Information in Vision-Language Models
  4. Positional Encoding in Transformers
  5. Vision Transformer's Spatial Understanding
  6. CLIP's Cross-Modal Position Understanding
  7. Approaches to Spatial Understanding in Computer Vision
  8. Traditional Vision Backbone Approaches
  9. Attention-Based Spatial Understanding
  10. Cross-Modal Spatial Understanding
  11. Direct Coordinate Prediction Approaches
  12. Visual Position Encoding and Grid-Based Approaches
  13. Position Encoding Evolution in Vision Models
  14. Grid-Based References in Computer Vision
  15. Spatial Understanding in Multimodal Systems
  16. ...and 16 more sections

Figures (5)

  • Figure 1: Visualization of our grid-based spatial reference system. (a) Shows the original image without any modification. (b) Demonstrates the same image overlaid with our proposed $9 \times 9$ black grid pattern at $0.3$ transparency level.
  • Figure 2: Visualization results comparing search bar bounding box predictions. Left: Prediction on original image without grid overlay shows significant deviation. Right: Prediction with our $9\times9$ grid system demonstrates improved localization accuracy.
  • Figure 3: Visualization of person localization results across nine different scenarios. For each pair, the left image shows predictions on the original image, while the right image displays predictions with a 9×9 grid overlay, demonstrating improved localization accuracy with the grid-based approach.
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