Summary of Beyond the Doors Of Perception: Vision Transformers Represent Relations Between Objects, by Michael A. Lepori et al.
Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects
by Michael A. Lepori, Alexa R. Tartaglini, Wai Keen Vong, Thomas Serre, Brenden M. Lake, Ellie Pavlick
First submitted to arxiv on: 22 Jun 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 The paper investigates the performance of vision transformers (ViTs) on tasks involving visual relations, which are surprisingly difficult for them despite their state-of-the-art performance in various settings. By adopting mechanistic interpretability methods, researchers study how ViTs perform abstract visual reasoning, focusing on a relational reasoning task: judging whether two visual entities are the same or different. The findings suggest that pretrained ViTs fine-tuned on this task exhibit two stages of processing: perceptual and relational. In the second stage, ViTs can learn to represent abstract visual relations, which has long been considered challenging for artificial neural networks. The paper highlights the importance of understanding ViTs in terms of discrete processing stages to diagnose and rectify shortcomings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how a type of AI called vision transformers (ViTs) do when trying to figure out if two pictures are the same or different. Despite being really good at other tasks, ViTs struggle with this one. The researchers want to know how ViTs work when doing these kinds of tasks, so they study how it processes information in stages. They find that ViTs have a first stage where they look at local features and then a second stage where they compare the features to decide if the pictures are the same or different. This is important because it can help us understand why some AI models don’t do as well as others. |
