Self-supervised learning (SSL) strategies have demonstrated remarkable
performance in various recognition tasks. However, both our preliminary
investigation and recent studies suggest that they may be less effective in
learning representations for fine-grained visual recognition (FGVR) since many
features helpful for optimizing SSL objectives are not suitable for
characterizing the subtle differences in FGVR. To overcome this issue, we
propose learning an additional screening mechanism to identify discriminative
clues commonly seen across instances and classes, dubbed as common rationales
in this paper. Intuitively, common rationales tend to correspond to the
discriminative patterns from the key parts of foreground objects. We show that
a common rationale detector can be learned by simply exploiting the GradCAM
induced from the SSL objective without using any pre-trained object parts or
saliency detectors, making it seamlessly to be integrated with the existing SSL
process. Specifically, we fit the GradCAM with a branch with limited fitting
capacity, which allows the branch to capture the common rationales and discard
the less common discriminative patterns. At the test stage, the branch
generates a set of spatial weights to selectively aggregate features
representing an instance. Extensive experimental results on four visual tasks
demonstrate that the proposed method can lead to a significant improvement in
different evaluation settings.

本文提出了一种名为 common rationales 的机制，将其作为特征筛选机制来增强自监督学习方法在细粒度图像识别任务中的效果，并表明该方法在四种视觉任务中均取得了显著的改进。