Meta-collaborative comparison for effective cross-domain few-shot learning

Recent advancements in cross-domain few-shot learning (CD-FSL) primarily focus on learning to compare global representations between query and support images for classification. However, due to the notorious cross-domain semantic gap, the ideal global representations can be totally different across domains, thereby solely learning to compare global representations is not sufficient to achieve effective generalization in challenging cases. To mitigate this problem, we present a Meta-collaborative Comparison Network (MeCo-Net) for CD-FSL, which imitates humans to recognize unfamiliar objects through collaborative comparison on both global and local representations. Following this idea, paralleling with a conventional global comparison branch, we additionally feed random crops of both query and support images into a feature encoder to separately extract their local representations. Subsequently, we associate these local representations across images through bipartite graph matching for local comparison. Thanks to the complementary global and local comparisons, we can obtain a more generalizable classifier for CD-FSL by meta-integrating them for final prediction. Experimental results on eight benchmarks demonstrate that the proposed model generalizes to multiple target domains with state-of-the-art performance without the need for fine-tuning.