Summary of Concept Visualization: Explaining the Clip Multi-modal Embedding Using Wordnet, by Loris Giulivi et al.
Concept Visualization: Explaining the CLIP Multi-modal Embedding Using WordNet
by Loris Giulivi, Giacomo Boracchi
First submitted to arxiv on: 23 May 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 advancement in multi-modal embeddings called CLIP has led to significant breakthroughs in Computer Vision tasks. However, its opaque architecture may limit the adoption of models using CLIP as their backbone, particularly in fields like medicine where model explainability is crucial. Existing explanation methods for Computer Vision models rely on Saliency Maps generated through gradient analysis or input perturbation, but these maps can only be computed to explain classes relevant to the end task, leaving a substantial portion of the learned representations unexplained. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a special computer program that can look at pictures and understand what’s in them. This program is called CLIP, and it’s really good at recognizing things in photos. But there’s a problem – we don’t always know why it makes certain decisions. That’s important to fix, especially when we’re using these programs for tasks like medical diagnosis. Right now, we can only explain why the program thinks something is in a picture if it’s related to what we’re trying to find. The rest of the information is a mystery. |
Keywords
» Artificial intelligence » Multi modal
