Summary of Embspatial-bench: Benchmarking Spatial Understanding For Embodied Tasks with Large Vision-language Models, by Mengfei Du et al.
EmbSpatial-Bench: Benchmarking Spatial Understanding for Embodied Tasks with Large Vision-Language Models
by Mengfei Du, Binhao Wu, Zejun Li, Xuanjing Huang, Zhongyu Wei
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent surge in Large Vision-Language Models (LVLMs) has shown promise for applications such as embodied scene understanding. However, the gap between current LVLMs and true embodied intelligence remains unknown. To address this, we introduce EmbSpatial-Bench, a benchmark for evaluating spatial understanding of LVLMs. This benchmark is derived from real-world scenes and covers 6 essential spatial relationships from an egocentric perspective. Our experiments demonstrate the limitations of current LVLMs, including GPT-4V, in understanding embodied spatial relationships. To bridge this gap, we propose EmbSpatial-SFT, a dataset designed to improve LVLMs’ spatial understanding capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Embodied scene understanding is a critical skill that allows AI models like Large Vision-Language Models (LVLMs) to understand and interact with the world around them. Currently, there’s a big gap between what LVLMs can do and true embodied intelligence. To help fill this gap, researchers have created a benchmark called EmbSpatial-Bench. This benchmark is designed to test how well LVLMs can understand spatial relationships in real-world scenes. The results show that current LVLMs are not very good at this task. To improve their abilities, scientists have developed a new dataset called EmbSpatial-SFT. |
Keywords
» Artificial intelligence » Gpt » Scene understanding
