Summary of Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance Of 2d Representation, Positions, and Objects, by Wenhao Li et al.
Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects
by Wenhao Li, Yudong Xu, Scott Sanner, Elias Boutros Khalil
First submitted to arxiv on: 8 Oct 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 A recent study explores the capabilities of Vision Transformers (ViTs) in solving visual reasoning tasks on the Abstraction and Reasoning Corpus (ARC). The ARC benchmark requires AI systems to solve program synthesis problems over small 2D images using a few input-output training pairs. Despite being a state-of-the-art model for images, ViTs struggle to learn the implicit mapping between input and output images in most ARC tasks, even when trained on one million examples per task. This highlights an inherent representational deficiency of the ViT architecture that makes it incapable of uncovering the simple structured mappings underlying the ARC tasks. To address this limitation, the authors propose a novel ViT-style architecture called ViTARC, which incorporates enhancements such as pixel-level input representation, spatially-aware tokenization, and object-based positional encoding leveraging automatic segmentation. The task-specific ViTARC models achieve a test solve rate close to 100% on more than half of the 400 public ARC tasks through supervised learning from input-output grids. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Abstraction and Reasoning Corpus (ARC) is a special benchmark for testing Artificial Intelligence systems that can understand and solve visual reasoning problems. Researchers tried using a powerful AI model called Vision Transformer (ViT) to solve these problems, but it didn’t work well even when they gave it lots of training data. This means the ViT model has some limitations that make it hard for it to learn certain things. To fix this, scientists created a new version of the ViT model, called ViTARC, which helps it understand visual reasoning better. The new model is very good at solving many ARC problems and shows that even with lots of training data, AI systems need special tools to be great at understanding and solving certain kinds of problems. |
Keywords
» Artificial intelligence » Positional encoding » Supervised » Tokenization » Vision transformer » Vit
