Summary of Lost in Space: Probing Fine-grained Spatial Understanding in Vision and Language Resamplers, by Georgios Pantazopoulos et al.
Lost in Space: Probing Fine-grained Spatial Understanding in Vision and Language Resamplers
by Georgios Pantazopoulos, Alessandro Suglia, Oliver Lemon, Arash Eshghi
First submitted to arxiv on: 21 Apr 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 This paper proposes an innovative method for combining frozen large language models (LLMs) and visual encoders. The approach involves a resampler module that generates a “visual prompt” which is used in conjunction with the textual prompt to drive LLM performance. While this method has shown impressive results across coarse-grained tasks like image captioning, the paper examines its effectiveness for more fine-grained tasks requiring spatial understanding. To assess the visual prompt’s ability to encode spatial information, the authors employ diagnostic classifiers. Results indicate that the resampler output lacks spatial information when kept frozen during training. However, joint training of the resampler and classifier yields significant performance gains, demonstrating the potential for LLMs to encode spatial information with proper pretraining objectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to combine language models and visual encoders to create better image understanding systems. The current method does well on simple tasks like describing images, but struggles when it comes to more complex tasks that require knowing the layout of objects in an image. To fix this, researchers tested how well the system can learn about spatial information by using special classifiers. They found that when they trained the system jointly, the performance greatly improved. This shows that the language models have the potential to understand spatial information if taught correctly. |
Keywords
» Artificial intelligence » Image captioning » Pretraining » Prompt
