Inversed Pyramid Network with Spatial-adapted and Task-oriented Tuning for few-shot learning

With the rapid development of artificial intelligence, deep neural networks have achieved great performance in many tasks. However, traditional deep learning methods require a large amount of training data, which may not be available in certain practical scenarios. In contrast, few-shot learning aims to learn a model that can be readily adapted to new unseen classes from only one or a few labeled examples. Despite this success, most existing methods rely on pre-trained feature extractor networks trained with global features, ignoring the discrimination of local features, and weak generalization capabilities limit their performance. To address the problem, according to the human's coarse-to-fine cognition paradigm, we propose an Inverted Pyramid Network with Spatial-adapted and Task-oriented Tuning (TIPN) for few-shot learning. Specifically, the proposed framework represents local features for categories that are difficult to distinguish by global features and recognizes objects from both global and local perspectives. Moreover, to ensure the calibration validity of the proposed model at the local stage, we introduce the Spatial-adapted Layer to preserve the discriminative global representation ability of the pre-trained backbone network. Meanwhile, as the representations extracted from the past categories are not applicable to the current new tasks, we further propose the Task-oriented Tuning strategy to adjust the parameters of the Batch Normalization layer in the pre-trained feature extractor network, to explicitly transfer knowledge from base classes to novel classes according to the support samples of each task. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method can significantly outperform many state-of-the-art few-shot learning methods.