Summary of Vitree: Single-path Neural Tree For Step-wise Interpretable Fine-grained Visual Categorization, by Danning Lao et al.
ViTree: Single-path Neural Tree for Step-wise Interpretable Fine-grained Visual Categorization
by Danning Lao, Qi Liu, Jiazi Bu, Junchi Yan, Wei Shen
First submitted to arxiv on: 30 Jan 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 As deep learning models continue to excel in various applications, the need for interpretability becomes increasingly important. Existing methods often rely on post-hoc techniques or prototypes, which can be indirect and lack intrinsic illustration. In this research, we introduce ViTree, a novel approach that combines the vision transformer as a feature extraction backbone with neural decision trees. ViTree traverses tree paths to select informative local regions from transformer-processed features, refining representations in a step-wise manner. Unlike previous models, ViTree selects a single tree path, offering a clearer and simpler decision-making process. This patch and path selectivity enhances model interpretability, enabling better insights into the model’s inner workings. The approach surpasses strong competitors and achieves state-of-the-art performance while maintaining exceptional interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how an AI model makes decisions. Most models are like black boxes – we don’t know exactly what they’re doing. In this research, scientists created a new way called ViTree that helps us see inside the model’s decision-making process. They combined two ideas: a popular machine learning technique and a type of tree-like structure. This lets them select specific parts of the image that are important for making decisions. The result is a clearer and simpler way to understand how the model works, which can be really useful in many applications. |
Keywords
» Artificial intelligence » Deep learning » Feature extraction » Machine learning » Transformer » Vision transformer
