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.
@article{arxiv.2410.09797,
title = {Task Adaptive Feature Distribution Based Network for Few-shot Fine-grained Target Classification},
author = {Ping Li and Hongbo Wang and Lei Lu},
journal= {arXiv preprint arXiv:2410.09797},
year = {2024}
}
Comments
The presentation logic of the algorithm section in the paper is unclear, and there are errors in the experimental part that need to be corrected, along with additional experiments to be conducted