Task Adaptive Feature Distribution Based Network for Few-shot Fine-grained Target Classification

• URL: http://arxiv.org/abs/2410.09797v2
• Date: Mon, 18 Nov 2024 06:44:30 GMT
• Title: Task Adaptive Feature Distribution Based Network for Few-shot Fine-grained Target Classification
• Authors: Ping Li, Hongbo Wang, Lei Lu,
• Abstract summary: We propose TAFD-Net: a task adaptive feature distribution network. It features a task-adaptive component for embedding to capture task-level nuances, an asymmetric metric for calculating feature distribution similarities between query samples and support categories, and a contrastive measure strategy to boost performance.
• Score: 16.575362884459963
• License:
• Abstract: Metric-based few-shot fine-grained classification has shown promise due to its simplicity and efficiency. However, existing methods often overlook task-level special cases and struggle with accurate category description and irrelevant sample information. To tackle these, we propose TAFD-Net: a task adaptive feature distribution network. It features a task-adaptive component for embedding to capture task-level nuances, an asymmetric metric for calculating feature distribution similarities between query samples and support categories, and a contrastive measure strategy to boost performance. Extensive experiments have been conducted on three datasets and the experimental results show that our proposed algorithm outperforms recent incremental learning algorithms.

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