Few-Shot Fine-Grained Classification With Foreground-Aware Kernelized Feature Reconstruction Network

Feature reconstruction networks have achieved remarkable performance in few-shot fine-grained classification tasks. Nonetheless, traditional feature reconstruction networks rely on linear regression. This linearity may cause the loss of subtle discriminative cues, ultimately resulting in less precise reconstructed features. Moreover, in situations where the background predominantly occupies the image, the background reconstruction errors tend to overshadow foreground reconstruction errors, resulting in inaccurate reconstruction errors. In order to address the two key issues, a novel approach called the Foreground-Aware Kernelized Feature Reconstruction Network (FKFRN) is proposed. Specifically, to address the problem of imprecise reconstructed features, we introduce kernel methods into linear feature reconstruction, extending it to nonlinear feature reconstruction, thus enabling the reconstruction of richer, finer-grained discriminative features. To tackle the issue of inaccurate reconstruction errors, the foreground-aware reconstruction error is proposed. Specifically, the model assigns higher weights to features containing more foreground information and lower weights to those dominated by background content, which reduces the impact of background errors on the overall reconstruction. To estimate these weights accurately, we design two complementary strategies: an explicit probabilistic graphical model and an implicit neural network-based approach. Extensive experimental results on eight datasets validate the effectiveness of the proposed approach for few-shot fine-grained classification.