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arXiv · 2504.12104

Logits DeConfusion with CLIP for Few-Shot Learning

Shuo Li·Fang Liu·Zehua Hao·Xinyi Wang·Lingling Li·Xu Liu·Puhua Chen·Wenping Ma
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Abstract

With its powerful visual-language alignment capability, CLIP performs well in zero-shot and few-shot learning tasks. However, we found in experiments that CLIP's logits suffer from serious inter-class confusion problems in downstream tasks, and the ambiguity between categories seriously affects the accuracy. To address this challenge, we propose a novel method called Logits DeConfusion, which effectively learns and eliminates inter-class confusion in logits by combining our Multi-level Adapter Fusion (MAF) module with our Inter-Class Deconfusion (ICD) module. Our MAF extracts features from different levels and fuses them uniformly to enhance feature representation. Our ICD learnably eliminates inter-class confusion in logits with a residual structure. Experimental results show that our method can significantly improve the classification performance and alleviate the inter-class confusion problem. The code is available at https://github.com/LiShuo1001/LDC.

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Shuo Li, Fang Liu, Zehua Hao, Xinyi Wang, Lingling Li, Xu Liu, Puhua Chen, Wenping Ma. 2025-04-16. Logits DeConfusion with CLIP for Few-Shot Learning. https://arxiv.org/abs/2504.12104

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