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

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.

References in corpus (11)

• Adam: A Method for Stochastic Optimization
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• Prototypical Networks for Few-shot Learning
• Spatial-Temporal Recurrent Neural Network for Emotion Recognition
• Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
• Few-Shot Learning with Graph Neural Networks
• Multi-level Semantic Feature Augmentation for One-shot Learning
• A Two-Stage Approach to Few-Shot Learning for Image Recognition
• Knowledge Graph Transfer Network for Few-Shot Recognition
• Automated Relational Meta-learning
• Baby steps towards few-shot learning with multiple semantics

Cited by in corpus (2)

• Memorizing Complementation Network for Few-Shot Class-Incremental Learning
• TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning