Summary of Intcoop: Interpretability-aware Vision-language Prompt Tuning, by Soumya Suvra Ghosal et al.
IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning
by Soumya Suvra Ghosal, Samyadeep Basu, Soheil Feizi, Dinesh Manocha
First submitted to arxiv on: 19 Jun 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 This paper presents a novel approach to prompt-tuning for image-text contrastive models like CLIP. Current methods require manual engineering of prompts, which can be time-consuming and tedious. The proposed method, IntCoOp, learns to align attribute-level inductive biases and class embeddings during prompt-tuning, leading to superior performance on downstream tasks such as generalization to novel classes and unseen domain shifts. Evaluation across 10 datasets demonstrates an average improvement of 7.35% compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computer models better at understanding images and words together. Right now, we have to do a lot of work to get the right words to match with the right pictures. The authors of this paper found that if they include special details about what’s in an image (like “green” tree frog), they can make the model work way better. They created a new method called IntCoOp, which helps the model learn to understand these details and use them to get even better results. This is important because it could help us make computers that are more like how we humans think. |
Keywords
» Artificial intelligence » Generalization » Prompt
