Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning

TL;DR

This paper introduces MAP-Net, a novel approach for Few-Shot Learning that addresses information asymmetry between labeled and unlabeled samples by building symmetry in visual and semantic modalities through graph propagation and relation guidance.

Contribution

The paper proposes MAP-Net, which supplements semantic information for unlabeled samples and rectifies feature embeddings to achieve modality symmetry in Few-Shot Learning.

Findings

Outperforms state-of-the-art on three semantic-labeled datasets.

Effectively generates pseudo-semantics for unlabeled samples.

Improves intra-class consistency and inter-class discriminability.

Abstract

Semantic information provides intra-class consistency and inter-class discriminability beyond visual concepts, which has been employed in Few-Shot Learning (FSL) to achieve further gains. However, semantic information is only available for labeled samples but absent for unlabeled samples, in which the embeddings are rectified unilaterally by guiding the few labeled samples with semantics. Therefore, it is inevitable to bring a cross-modal bias between semantic-guided samples and nonsemantic-guided samples, which results in an information asymmetry problem. To address this problem, we propose a Modal-Alternating Propagation Network (MAP-Net) to supplement the absent semantic information of unlabeled samples, which builds information symmetry among all samples in both visual and semantic modalities. Specifically, the MAP-Net transfers the neighbor information by the graph propagation to generate the pseudo-semantics for unlabeled samples guided by the completed visual relationships and rectify the feature embeddings. In addition, due to the large discrepancy between visual and semantic modalities, we design a Relation Guidance (RG) strategy to guide the visual relation vectors via semantics so that the propagated information is more beneficial. Extensive experimental results on three semantic-labeled datasets, i.e., Caltech-UCSD-Birds 200-2011, SUN Attribute Database, and Oxford 102 Flower, have demonstrated that our proposed method achieves promising performance and outperforms the state-of-the-art approaches, which indicates the necessity of information symmetry.