Boosting Few-Shot Learning via Attentive Feature Regularization
Xingyu Zhu Shuo Wang Jinda Lu Yanbin Hao Haifeng Liu Xiangnan He
Few-shot learning (FSL) based on manifold regularization aims to improve the
recognition capacity of novel objects with limited training samples by mixing
two samples from different categories with a blending factor. However, this
mixing operation weakens the feature representation due to the linear
interpolation and the overlooking of the importance of specific channels. To
solve these issues, this paper proposes attentive feature regularization (AFR)
which aims to improve the feature representativeness and discriminability. In
our approach, we first calculate the relations between different categories of
semantic labels to pick out the related features used for regularization. Then,
we design two attention-based calculations at both the instance and channel
levels. These calculations enable the regularization procedure to focus on two
crucial aspects: the feature complementarity through adaptive interpolation in
related categories and the emphasis on specific feature channels. Finally, we
combine these regularization strategies to significantly improve the classifier
performance. Empirical studies on several popular FSL benchmarks demonstrate
the effectiveness of AFR, which improves the recognition accuracy of novel
categories without the need to retrain any feature extractor, especially in the
1-shot setting. Furthermore, the proposed AFR can seamlessly integrate into
other FSL methods to improve classification performance.