LPN: Language-guided Prototypical Network for few-shot classification

TL;DR

This paper introduces LPN, a novel few-shot classification model that integrates vision and language modalities using a pre-trained text encoder and a language-guided decoder to improve classification accuracy.

Contribution

LPN leverages multi-modality information with a dual-branch architecture, combining visual and language features for enhanced few-shot learning performance.

Findings

LPN outperforms state-of-the-art methods on benchmark datasets.

The integration of language features improves classification robustness.

The proposed method effectively aligns visual and textual features for better prototypes.

Abstract

Few-shot classification aims to adapt to new tasks with limited labeled examples. To fully use the accessible data, recent methods explore suitable measures for the similarity between the query and support images and better high-dimensional features with meta-training and pre-training strategies. However, the potential of multi-modality information has barely been explored, which may bring promising improvement for few-shot classification. In this paper, we propose a Language-guided Prototypical Network (LPN) for few-shot classification, which leverages the complementarity of vision and language modalities via two parallel branches to improve the classifier. Concretely, to introduce language modality with limited samples in the visual task, we leverage a pre-trained text encoder to extract class-level text features directly from class names while processing images with a conventional image encoder. Then, we introduce a language-guided decoder to obtain text features corresponding to each image by aligning class-level features with visual features. Additionally, we utilize class-level features and prototypes to build a refined prototypical head, which generates robust prototypes in the text branch for follow-up measurement. Furthermore, we leverage the class-level features to align the visual features, capturing more class-relevant visual features. Finally, we aggregate the visual and text logits to calibrate the deviation of a single modality, enhancing the overall performance. Extensive experiments demonstrate the competitiveness of LPN against state-of-the-art methods on benchmark datasets.