Jingshan Hong

MaskAnyNet: Rethinking Masked Image Regions as Valuable Information in Supervised Learning

In supervised learning, traditional image masking faces two key issues: (i) discarded pixels are underutilized, leading to a loss of valuable contextual information; (ii) masking may remove small or critical features, especially in fine-grained tasks. In contrast, masked image modeling (MIM) has dem