MHFC: Multi-Head Feature Collaboration for Few-Shot Learning

• URL: http://arxiv.org/abs/2109.07785v1
• Date: Thu, 16 Sep 2021 08:09:35 GMT
• Title: MHFC: Multi-Head Feature Collaboration for Few-Shot Learning
• Authors: Shuai Shao, Lei Xing, Yan Wang, Rui Xu, Chunyan Zhao, Yan-Jiang Wang, Bao-Di Liu
• Abstract summary: Few-shot learning aims to address the data-scarce problem. We propose Multi-Head Feature Collaboration (MHFC) algorithm, which attempts to project the multi-head features to a unified space. We evaluate the proposed method on five benchmark datasets and achieve significant improvements of 2.1%-7.8% compared with state-of-the-arts.
• Score: 17.699793591135904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot learning (FSL) aims to address the data-scarce problem. A standard FSL framework is composed of two components: (1) Pre-train. Employ the base data to generate a CNN-based feature extraction model (FEM). (2) Meta-test. Apply the trained FEM to acquire the novel data's features and recognize them. FSL relies heavily on the design of the FEM. However, various FEMs have distinct emphases. For example, several may focus more attention on the contour information, whereas others may lay particular emphasis on the texture information. The single-head feature is only a one-sided representation of the sample. Besides the negative influence of cross-domain (e.g., the trained FEM can not adapt to the novel class flawlessly), the distribution of novel data may have a certain degree of deviation compared with the ground truth distribution, which is dubbed as distribution-shift-problem (DSP). To address the DSP, we propose Multi-Head Feature Collaboration (MHFC) algorithm, which attempts to project the multi-head features (e.g., multiple features extracted from a variety of FEMs) to a unified space and fuse them to capture more discriminative information. Typically, first, we introduce a subspace learning method to transform the multi-head features to aligned low-dimensional representations. It corrects the DSP via learning the feature with more powerful discrimination and overcomes the problem of inconsistent measurement scales from different head features. Then, we design an attention block to update combination weights for each head feature automatically. It comprehensively considers the contribution of various perspectives and further improves the discrimination of features. We evaluate the proposed method on five benchmark datasets (including cross-domain experiments) and achieve significant improvements of 2.1%-7.8% compared with state-of-the-arts.

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