Distribution estimation is a pivotal strategy in few-shot learning (FSL) to mitigate data scarcity by sampling from estimated distributions, utilizing statistical properties (mean and variance) transferred from related base categories. However, category-level estimation alone often fails to generate representative samples due to significant dissimilarities between base and novel categories, leading to suboptimal performance. To address this limitation, we propose Hybrid Granularity Distribution Estimation (HGDE), which integrates both coarse-grained category-level statistics and fine-grained instance-level statistics. By leveraging instance statistics from the nearest base samples, HGDE enhances the characterization of novel categories, capturing subtle features that category-level estimation overlooks. These statistics are fused through linear interpolation to form a robust distribution for novel categories, ensuring both diversity and representativeness in generated samples. Additionally, HGDE employs refined estimation techniques, such as weighted summation for mean calculation and principal component retention for covariance, to further improve accuracy. Empirical evaluations on four FSL benchmarks, including Mini-ImageNet, Tiered-ImageNet, CUB and CIFAR-FS, demonstrate that HGDE offers effective distribution estimation capabilities and leads to notable accuracy gains, with improvements of more than 1.8% in 1-shot tasks on CUB. These results highlight HGDE's ability to balance mean precision and variance diversity, making it a versatile and effective solution for FSL.
Wang et al. (Thu,) studied this question.