SPARE: Self-supervised part erasing for ultra-fine-grained visual categorization

•We propose a novel framework SPARE that segments parts using only image-level category labels and produces discriminative part feature representations for ultra-fine-grained visual categorization.•A new self-supervised module is proposed to generate more diversified part segments with semantic meaning, and enhance the discriminability via predicting the contextual position of the erased parts.•Very encouraging experimental results demonstrate the effectiveness of SPARE and the possibility in advancing research for ultra-fine-grained visual categorization.