Attribute-guided Dynamic Routing Graph Network for Transductive Few-shot Learning

Motivated by the structured form of human cognition, attributes have been introduced in few-shot classification to learn more representative sample features. However, existing attribute-based methods usually treat the importance of different attributes as equals to conclude the sample relations, which cannot distinguish the classes with many similar attributes well. In order to address this problem, we propose an Attribute-guided Dynamic Routing Graph Network (ADRGN) to explicitly learn task-dependent attribute importance scores to help explore the sample relations in a fine-grained manner for adaptive graph-based inference. Specifically, we first leverage a CNN backbone and a transformation network to generate attribute-specific sample representations according to attribute annotations. Next, we treat the attribute-specific sample representations as visual primary capsules and employ an inter-sample routing to explore the visual diversity of each attribute in the current task. Based on the generated diversity capsules, we perform an inter-attribute routing to explore the relations between different attributes to predict the visual attribute importance scores. Meanwhile, we design an attribute semantic routing module to predict the semantic attribute importance from the semantic attribute embeddings to help the learning of the visual attribute importance prediction with a knowledge distillation strategy. Finally, we utilize the visual attribute importance scores to adaptively aggregate sample similarities computed based on the attribute-specific representations to capture the global fine-grained sample relations for message passing and graph-based inference. Experimental results on three few-shot classification benchmarks show that the proposed ADRGN obtains state-of-the-art performance.