Summary of A Comparative Survey Of Vision Transformers For Feature Extraction in Texture Analysis, by Leonardo Scabini et al.
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis
by Leonardo Scabini, Andre Sacilotti, Kallil M. Zielinski, Lucas C. Ribas, Bernard De Baets, Odemir M. Bruno
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 abstract explores the performance of Vision Transformers (ViTs) in texture recognition tasks, which has not been thoroughly investigated despite their success in object recognition. The study reviews 21 different ViT variants and compares them with Convolutional Neural Networks (CNNs) and hand-engineered models on various texture analysis tasks. The goal is to understand the potential and differences among these models when applied directly to texture recognition, using pre-trained ViTs for feature extraction and linear classifiers for evaluation. The results show that ViTs generally outperform CNNs and hand-engineered models, especially with stronger pre-training and in-the-wild textures. Promising models include ViT-B with DINO pre-training, BeiTv2, Swin architecture, and EfficientFormer as a low-cost alternative. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares Vision Transformers (ViTs) to Convolutional Neural Networks (CNNs) for recognizing textures. It reviews many different types of ViTs and tests them on tasks like identifying texture patterns and detecting changes in texture. The results show that ViTs are often better than CNNs at these tasks, especially when the images are from everyday life. Some models perform particularly well, such as a combination of ViT-B and DINO training, BeiTv2, Swin, and EfficientFormer. |
Keywords
» Artificial intelligence » Feature extraction » Vit
