Summary of Understanding Visual Concepts Across Models, by Brandon Trabucco et al.
Understanding Visual Concepts Across Models
by Brandon Trabucco, Max Gurinas, Kyle Doherty, Ruslan Salakhutdinov
First submitted to arxiv on: 11 Jun 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 explores the fine-tuning of large multimodal models, such as Stable Diffusion, to generate, detect, and classify new visual concepts. It investigates whether these models learn similar words for the same concepts by conducting a large-scale analysis on three state-of-the-art models in text-to-image generation, open-set object detection, and zero-shot classification. The results show that new word embeddings are model-specific and non-transferable, and perturbations within an ε-ball to any prior embedding can generate, detect, and classify an arbitrary concept. Soft prompt-tuning approaches can find these perturbative solutions when applied to visual concept learning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big models like Stable Diffusion work with words and pictures. It wants to know if the model learns the same words for similar things (like “orange cat”). The researchers tested three special models on different tasks, and found that each model learns its own way of linking words to pictures. This means that if you take a word from one model, it won’t work with another model’s picture. |
Keywords
» Artificial intelligence » Classification » Diffusion » Embedding » Fine tuning » Image generation » Object detection » Prompt » Zero shot
