用分类器引导的多视角蒸馏，提升无监督多标签图像分类效果
Classifier-guided CLIP Distillation for Unsupervised Multi-label Classification
- 利用分类器激活图指导选择目标附近多个视角
- 通过去偏伪标签提升预测准确性，显著优于现有方法
- 无需额外标注，适合图像理解与自动标注场景
多标签分类对全面理解图像至关重要，但精确标注成本高。近期研究提出利用强大的视觉-语言模型CLIP进行无监督多标签分类。尽管CLIP表现优异，仍存在视图依赖性预测和固有偏差问题。本文提出分类器引导的CLIP蒸馏（CCD）方法：基于分类器的类别激活图（CAM）选取目标附近的多个局部视图，并对CLIP生成的伪标签进行去偏处理。该方法无需额外标注即可选择多视角并改善预测偏差，实验验证其在多个数据集上均优于现有技术。代码已开源。
原文摘要 · Abstract (English)
Multi-label classification is crucial for comprehensive image understanding, yet acquiring accurate annotations is challenging and costly. To address this, a recent study suggests exploiting unsupervised multi-label classification leveraging CLIP, a powerful vision-language model. Despite CLIP's proficiency, it suffers from view-dependent predictions and inherent bias, limiting its effectiveness. We propose a novel method that addresses these issues by leveraging multiple views near target objects, guided by Class Activation Mapping (CAM) of the classifier, and debiasing pseudo-labels derived from CLIP predictions. Our Classifier-guided CLIP Distillation (CCD) enables selecting multiple local views without extra labels and debiasing predictions to enhance classification performance. Experimental results validate our method's superiority over existing techniques across diverse datasets. The code is available at https://github.com/k0u-id/CCD.
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