Summary of Enhancing Clip Conceptual Embedding Through Knowledge Distillation, by Kuei-chun Kao
Enhancing CLIP Conceptual Embedding through Knowledge Distillation
by Kuei-Chun Kao
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); 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 This paper presents an innovative approach called Knowledge-CLIP, which aims to improve the performance of CLIP’s text and image encoders in multi-modal contexts. By integrating a new knowledge distillation method based on Llama 2, Knowledge-CLIP focuses on three key objectives: Text Embedding Distillation, Concept Learning, and Contrastive Learning. The proposed model involves training the text encoder to mirror the teacher model, employing offline K-means clustering to assign soft concept labels, and aligning text and image embeddings through contrastive learning. Experimental results show that Knowledge-CLIP improves the performance of both text and image encoders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new AI model called Knowledge-CLIP to help computers understand images better. Right now, computers can match images with words, but they don’t really know what’s going on in those pictures. The new model uses an old model called Llama 2 to learn from text and images. It does this by copying the way Llama 2 thinks about text, learning what concepts are in each picture, and making sure the computer understands both words and images. This makes the computer better at understanding what’s happening in pictures. |
Keywords
» Artificial intelligence » Clustering » Distillation » Embedding » Encoder » K means » Knowledge distillation » Llama » Multi modal » Teacher model
