Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification

• URL: http://arxiv.org/abs/2103.11384v1
• Date: Sun, 21 Mar 2021 12:50:05 GMT
• Title: Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification
• Authors: Yaohui Li and Huaxiong Li and Haoxing Chen and Chunlin Chen
• Abstract summary: Few-shot image classification aims at recognizing unseen categories with a small number of labeled training data. Recent metric-based frameworks tend to represent a support class by a fixed prototype (e.g., the mean of the support category) and make classification according to the similarities between query instances and support prototypes. We propose a Hierarchical Representation based Query-Specific Prototypical Network (QPN) to tackle the limitations by generating a region-level prototype for each query sample.
• Score: 5.861206243996454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot image classification aims at recognizing unseen categories with a small number of labeled training data. Recent metric-based frameworks tend to represent a support class by a fixed prototype (e.g., the mean of the support category) and make classification according to the similarities between query instances and support prototypes. However, discriminative dominant regions may locate uncertain areas of images and have various scales, which leads to the misaligned metric. Besides, a fixed prototype for one support category cannot fit for all query instances to accurately reflect their distances with this category, which lowers the efficiency of metric. Therefore, query-specific dominant regions in support samples should be extracted for a high-quality metric. To address these problems, we propose a Hierarchical Representation based Query-Specific Prototypical Network (QPN) to tackle the limitations by generating a region-level prototype for each query sample, which achieves both positional and dimensional semantic alignment simultaneously. Extensive experiments conducted on five benchmark datasets (including three fine-grained datasets) show that our proposed method outperforms the current state-of-the-art methods.

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