Task context transformer and GCN for few-shot learning of cross-domain

Cross-Domain Few-Shot Learning (CD-FSL) to recognize new categories in a new domain with few samples has attracted significant attention. Recently, task-specific CD-FSL emerges as promising research for its great generalization. However, the existing task-specific CD-FSL methods are not robust enough in task context modeling and task-specific feature learning, especially under the cross-domain setting. To tackle this problem, a Task Context Transformer and Graph Convolutional Network (TCT-GCN) method for CD-FSL is proposed. The proposed TCT-GCN constructs three modules: 1) Multi-Level Feature Fusion, which fuses domain-shared low-level features into domain-unshared high-level features; 2) Transformer-based Task Context Encoder, which models sample order-independent task context; 3) Graph Convolutional Network-based Adaptive Feature Learner, which adaptively learns task-specific features. Experiments on eight CD-FSL datasets reveal the effectiveness of our TCT-GCN. & COPY; 2023 Elsevier B.V. All rights reserved.