Summary of An Empirical Analysis on Spatial Reasoning Capabilities Of Large Multimodal Models, by Fatemeh Shiri et al.
An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models
by Fatemeh Shiri, Xiao-Yu Guo, Mona Golestan Far, Xin Yu, Gholamreza Haffari, Yuan-Fang Li
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Large multimodal models (LMMs) have shown impressive performance across various vision and language tasks. However, their spatial reasoning capabilities remain under-explored. This paper constructs a novel VQA dataset, Spatial-MM, to comprehensively investigate LMMs’ spatial understanding and reasoning abilities. Our analysis reveals several key findings. Firstly, bounding boxes and scene graphs can significantly enhance LMMs’ spatial reasoning. Secondly, LMMs struggle more with questions posed from the human perspective than the camera perspective about an image. Thirdly, chain of thought (CoT) prompting does not improve model performance on complex multi-hop questions involving spatial relations. Additionally, our perturbation analysis shows that LMMs are stronger at basic object detection than complex spatial reasoning. This paper’s benchmark dataset and in-depth analyses can spark further research on LMMs’ spatial reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large multimodal models (LMMs) are very good at understanding images and words. But they don’t do as well when it comes to understanding how objects relate to each other in space. This paper makes a new dataset to help researchers study LMMs’ ability to understand spatial relationships. The results show that having more information about the objects in an image, like bounding boxes or scene graphs, can really help LMMs understand spaces better. It also shows that LMMs are better at recognizing simple objects than understanding complex spatial relationships. |
Keywords
» Artificial intelligence » Object detection » Prompting
