  Few-shot image classification has emerged as a key challenge in the field of
computer vision, highlighting the capability to rapidly adapt to new tasks with
minimal labeled data. Existing methods predominantly rely on image-level
features or local descriptors, often overlooking the holistic context
surrounding these descriptors. In this work, we introduce a novel approach
termed "Local Descriptor with Contextual Augmentation (LDCA)". Specifically,
this method bridges the gap between local and global understanding uniquely by
leveraging an adaptive global contextual enhancement module. This module
incorporates a visual transformer, endowing local descriptors with contextual
awareness capabilities, ranging from broad global perspectives to intricate
surrounding nuances. By doing so, LDCA transcends traditional descriptor-based
approaches, ensuring each local feature is interpreted within its larger visual
narrative. Extensive experiments underscore the efficacy of our method, showing
a maximal absolute improvement of 20\% over the next-best on fine-grained
classification datasets, thus demonstrating significant advancements in
few-shot classification tasks.
