@inproceedings{
yi2023leveraging,
title={Leveraging Cross-Modal Neighbor Representation for Improved {CLIP} Classification},
author={Chao Yi and Lu Ren and De-Chuan Zhan and Han-Jia Ye},
booktitle={R0-FoMo:Robustness of Few-shot and Zero-shot Learning in Large Foundation Models},
year={2023},
url={https://openreview.net/forum?id=pb0dRw3b97}
}
CLIP showcases exceptional cross-modal matching capabilities due to its training on text-image matching tasks. However, without specific optimization for unimodal scenarios, its performance in single-modality feature extraction might be suboptimal. Despite this, some studies have directly used CLIP's image encoder for tasks like few-shot classification, introducing a misalignment between its pre-training objectives and feature extraction methods. This inconsistency can diminish the quality of the image feature representation, adversely affecting CLIP's effectiveness in targeted tasks. In this paper, we view text features as precise neighbors of image features in CLIP's space and present a novel CrOss-moDal nEighbor Representation (CODER) based on the distance structure between images and their neighbor texts. This feature extraction method aligns better with CLIP's pre-training objectives, thereby fully leveraging CLIP's robust cross-modal capabilities. The key to constructing a high-quality CODER lies in how to create a vast amount of high-quality text to match with images. We introduce the Auto Prompt Generator (APG) to autonomously produce the required text in a data-free and training-free manner. We apply CODER to CLIP's zero-shot and few-shot image classification tasks. Experimental results across various datasets and architectures confirm CODER's effectiveness.
Leveraging Cross-Modal Neighbor Representation for Improved CLIP Classification
Chao Yi Lu Ren De-Chuan Zhan Han-Jia Ye