LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning
This work addresses the challenge of rapid adaptation to new tasks with minimal labeled data in computer vision, representing an incremental advancement over existing descriptor-based approaches.
The paper tackles the problem of few-shot image classification by introducing a method that enhances local descriptors with contextual awareness, achieving a maximal absolute improvement of 20% over the next-best method on fine-grained datasets.
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.