Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning

TL;DR

This paper introduces a novel dynamic kernel approach for few-shot learning that adapts to each task and sample, leading to improved classification and detection performance across multiple benchmarks.

Contribution

The paper proposes a task- and sample-aware dynamic kernel method for convolutional networks, enhancing rapid adaptation in few-shot learning scenarios.

Findings

Achieves state-of-the-art results on mini-ImageNet, tiered-ImageNet, CUB, and FC100.

Improves few-shot detection performance on MS COCO-PASCAL-VOC.

Demonstrates significant performance gains over baseline models.

Abstract

Learning and generalizing to novel concepts with few samples (Few-Shot Learning) is still an essential challenge to real-world applications. A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task. Dynamic networks have been shown capable of learning content-adaptive parameters efficiently, making them suitable for few-shot learning. In this paper, we propose to learn the dynamic kernels of a convolution network as a function of the task at hand, enabling faster generalization. To this end, we obtain our dynamic kernels based on the entire task and each sample and develop a mechanism further conditioning on each individual channel and position independently. This results in dynamic kernels that simultaneously attend to the global information whilst also considering minuscule details available. We empirically show that our model improves performance on few-shot classification and detection tasks, achieving a tangible improvement over several baseline models. This includes state-of-the-art results on 4 few-shot classification benchmarks: mini-ImageNet, tiered-ImageNet, CUB and FC100 and competitive results on a few-shot detection dataset: MS COCO-PASCAL-VOC.