FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?

• URL: http://arxiv.org/abs/2307.04114v1
• Date: Sun, 9 Jul 2023 08:07:43 GMT
• Title: FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?
• Authors: Zihao Jiang, Yunkai Dang, Dong Pang, Huishuai Zhang, Weiran Huang
• Abstract summary: Few-shot learning aims to train models that can be generalized to novel classes with only a few samples. We propose a novel few-shot learning framework that uses pre-trained language models based on contrastive learning.
• Score: 14.582209994281374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot learning aims to train models that can be generalized to novel classes with only a few samples. Recently, a line of works are proposed to enhance few-shot learning with accessible semantic information from class names. However, these works focus on improving existing modules such as visual prototypes and feature extractors of the standard few-shot learning framework. This limits the full potential use of semantic information. In this paper, we propose a novel few-shot learning framework that uses pre-trained language models based on contrastive learning. To address the challenge of alignment between visual features and textual embeddings obtained from text-based pre-trained language model, we carefully design the textual branch of our framework and introduce a metric module to generalize the cosine similarity. For better transferability, we let the metric module adapt to different few-shot tasks and adopt MAML to train the model via bi-level optimization. Moreover, we conduct extensive experiments on multiple benchmarks to demonstrate the effectiveness of our method.

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