Feature Aligning Few shot Learning Method Using Local Descriptors Weighted Rules
This work addresses challenges in few-shot learning for computer vision, such as redundant information and limited interpretability, but appears incremental as it builds on existing local descriptor methods.
The paper tackled few-shot image classification by proposing a method that aligns key local descriptors to remove background noise and preserve discriminative information, achieving state-of-the-art performance on three benchmark datasets in 1-shot and 5-shot settings.
Few-shot classification involves identifying new categories using a limited number of labeled samples. Current few-shot classification methods based on local descriptors primarily leverage underlying consistent features across visible and invisible classes, facing challenges including redundant neighboring information, noisy representations, and limited interpretability. This paper proposes a Feature Aligning Few-shot Learning Method Using Local Descriptors Weighted Rules (FAFD-LDWR). It innovatively introduces a cross-normalization method into few-shot image classification to preserve the discriminative information of local descriptors as much as possible; and enhances classification performance by aligning key local descriptors of support and query sets to remove background noise. FAFD-LDWR performs excellently on three benchmark datasets , outperforming state-of-the-art methods in both 1-shot and 5-shot settings. The designed visualization experiments also demonstrate FAFD-LDWR's improvement in prediction interpretability.