Summary of Leveraging Cross-modal Neighbor Representation For Improved Clip Classification, by Chao Yi et al.
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification
by Chao Yi, Lu Ren, De-Chuan Zhan, Han-Jia Ye
First submitted to arxiv on: 27 Apr 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 CLIP, a well-known cross-modal model, has been successfully applied to various tasks. However, its performance in single-modality feature extraction might be suboptimal without specific optimization. Researchers have used CLIP’s image encoder for few-shot classification, which can lead to misalignment between pre-training objectives and feature extraction methods. This inconsistency can negatively impact the quality of image features and affect the model’s effectiveness. To address this issue, a novel feature extraction method called CrOss-moDal nEighbor Representation (CODER) is proposed, aligning better with CLIP’s pre-training objectives. CODER leverages CLIP’s robust cross-modal capabilities by treating text features as precise neighbors of image features in its space. The key to constructing a high-quality CODER lies in generating diverse texts that match images. An Auto Text Generator (ATG) is introduced to automatically generate these texts without additional training or data. Experimental results across various datasets and models confirm CODER’s effectiveness for zero-shot and few-shot image classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A popular model called CLIP has been used in many applications, but it might not be as good at just looking at pictures by itself. Some people have tried to use a part of CLIP to help with recognizing images quickly, which can cause problems because the way it was trained doesn’t match how it’s being used. To solve this issue, researchers created a new method called CODER that makes better use of CLIP’s strengths. This method treats words and pictures as related concepts in the same space. The key to making CODER work well is to create lots of different texts that match with images. A special tool called Auto Text Generator helps generate these texts without needing more data or training. The results show that this new method, CODER, works well for recognizing images quickly and accurately. |
Keywords
» Artificial intelligence » Classification » Encoder » Feature extraction » Few shot » Image classification » Optimization » Zero shot
