Adaptive Task-Aware Refining Network for Few-Shot Fine-Grained Image Classification

The main challenge for Few-Shot Fine-Grained (FSFG) image classification is to learn discriminative feature representations with few labeled samples. In response to this challenge, task-aware few-shot learning methods have been introduced. However, existing approaches focus only on how to correlate task information with feature representations, while overlooking two critical issues. The first one is how to obtain accurate task representations with few labeled samples, to accurately get the feature region related to the task. The second one is how to reduce the impact of the background noise introduced in the process of acquiring feature regions, to alleviate the overfitting problem under the few-shot setting. To address these issues, we propose the Adaptive Task-Aware Refining Network (ATR-Net). Unlike previous approaches that use the center of class as task representations, ATR-Net enables the model to adaptively select task-specific information by interacting with the patches of all local features, resulting in a more accurate task representation. Moreover, a Channel Region-Aware Module (CRAM) and a Refinement Filtering Module (RFM) are designed to better acquire task-level information and instance-level information, to overcome the impact of background noise. We conduct extensive experiments on four public fine-grained datasets. The results demonstrate that the proposed method achieves superior performance. Our codes are anonymously available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://anonymous.4open.science/r/ATR-Net</uri>.