MaskAnyNet：将监督学习中的掩码图像区域重新思考为有价值的信息
MaskAnyNet: Rethinking Masked Image Regions as Valuable Information in Supervised Learning
AI总结：
针对监督学习中传统图像掩码存在的信息丢失和特征移除问题，提出MaskAnyNet，将掩码内容视为辅助知识，结合重学习机制利用可见与掩码信息，在多基准上实现CNN和Transformer骨干网络的性能提升并增强语义多样性。
摘要：
在监督学习中，传统图像掩码面临两个关键问题：（i）丢弃的像素未被充分利用，导致有价值的上下文信息丢失；（ii）掩码可能移除小或关键特征，尤其在细粒度任务中。相比之下，掩码图像建模（MIM）已证明掩码区域可从部分输入中重建，表明即使不完整数据也能与原始图像表现出强上下文一致性。这凸显了掩码区域作为语义多样性来源的潜力。受此启发，我们重新审视图像掩码方法，提出将掩码内容视为辅助知识而非忽略。基于此，我们提出MaskAnyNet，将掩码与重学习机制结合以利用可见和掩码信息。它可通过添加额外分支轻松扩展到任何模型，从重组的掩码区域联合学习。该方法利用掩码区域的语义多样性丰富特征并保留细粒度细节。在CNN和Transformer骨干网络上的实验显示在多个基准上的一致增益。进一步分析证实所提方法通过复用掩码内容提升了语义多样性。
英文摘要：
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 demonstrated that masked regions can be reconstructed from partial input, revealing that even incomplete data can exhibit strong contextual consistency with the original image. This highlights the potential of masked regions as sources of semantic diversity. Motivated by this, we revisit the image masking approach, proposing to treat masked content as auxiliary knowledge rather than ignored. Based on this, we propose MaskAnyNet, which combines masking with a relearning mechanism to exploit both visible and masked information. It can be easily extended to any model with an additional branch to jointly learn from the recomposed masked region. This approach leverages the semantic diversity of the masked regions to enrich features and preserve fine-grained details. Experiments on CNN and Transformer backbones show consistent gains across multiple benchmarks. Further analysis confirms that the proposed method improves semantic diversity through the reuse of masked content.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
