Summary of Pdiscoformer: Relaxing Part Discovery Constraints with Vision Transformers, by Ananthu Aniraj et al.
PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers
by Ananthu Aniraj, Cassio F.Dantas, Dino Ienco, Diego Marcos
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposes a novel approach to computer vision by using transformer-based vision models, such as DINOv2 ViT, to relax restrictive assumptions on object parts. Unlike previous methods that rely on fine-grained classification tasks, this method uses a total variation (TV) prior that allows for multiple connected components of any size. The authors test their approach on three fine-grained classification benchmarks: CUB, PartImageNet, and Oxford Flowers, and compare it to previously published methods, including PDiscoNet with a transformer-based backbone. The results show substantial improvements across the board, both in part discovery metrics and the downstream classification task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes computer vision more interpretable by allowing object parts to be any size or shape. It uses special types of artificial intelligence called transformers to find these parts without relying on small and compact ones like before. The authors tested their approach on pictures of birds, flowers, and other things and found that it worked better than previous methods. |
Keywords
» Artificial intelligence » Classification » Transformer » Vit
