Rectifying the Shortcut Learning of Background: Shared Object Concentration for Few-Shot Image Recognition
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Few-Shot image classification aims to utilize pretrained knowledge learned from a large-scale dataset to tackle a series of downstream classification tasks. Typically, each task involves only few training examples from brand-new categories. This requires the pretraining models to focus on well-generalizable knowledge, but ignore domain-specific information. In this paper, we observe that image background serves as a source of domain-specific knowledge, which is a shortcut for models to learn in the source dataset, but is harmful when adapting to brand-new classes. To prevent the model from learning this shortcut knowledge, we propose COSOC, a novel Few-Shot Learning framework, to automatically figure out foreground objects at both pretraining and evaluation stage. COSOC is a two-stage algorithm motivated by the observation that foreground objects from different images within the same class share more similar patterns than backgrounds. At the pretraining stage, for each class, we cluster contrastive-pretrained features of randomly cropped image patches, such that crops containing only foreground objects can be identified by a single cluster. We then force the pretraining model to focus on found foreground objects by a fusion sampling strategy; at the evaluation stage, among images in each training class of any few-shot task, we seek for shared contents and filter out background. The recognized foreground objects of each class are used to match foreground of testing images. Extensive experiments tailored to inductive FSL tasks on two benchmarks demonstrate the state-of-the-art performance of our method.
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