目标移除中的掩膜一致性正则化
Mask Consistency Regularization in Object Removal
AI总结：
针对扩散模型在目标移除任务中的掩膜幻觉和掩膜形状偏置问题，提出掩膜一致性正则化（MCR）训练策略，通过引入膨胀和重塑掩膜扰动并强制输出一致性，显著提升了修复结果的稳健性与上下文连贯性。
摘要：
目标移除是图像修复中的一项挑战性任务，涉及用匹配周围背景的内容无缝填充被移除的区域。尽管扩散模型取得了进展，但当前方法仍面临两个关键挑战。首先是掩膜幻觉，即模型在掩膜区域内生成无关或虚假内容；其次是掩膜形状偏置，即模型用模仿掩膜形状而非周围内容的物体填充掩膜区域。为解决这些问题，我们提出了掩膜一致性正则化（MCR），一种专为目标移除任务设计的新型训练策略。在训练期间，该方法引入两种掩膜扰动：膨胀和重塑，强制这些扰动分支的输出与原始掩膜之间保持一致性。膨胀掩膜有助于将模型输出与周围内容对齐，而重塑掩膜则鼓励模型打破掩膜形状偏置。这种策略组合使MCR能够产生更稳健且上下文更连贯的修复结果。实验表明，MCR显著减少了幻觉和掩膜形状偏置，提升了目标移除的性能。
英文摘要：
Object removal, a challenging task within image inpainting, involves seamlessly filling the removed region with content that matches the surrounding context. Despite advancements in diffusion models, current methods still face two critical challenges. The first is mask hallucination, where the model generates irrelevant or spurious content inside the masked region, and the second is mask-shape bias, where the model fills the masked area with an object that mimics the mask's shape rather than surrounding content. To address these issues, we propose Mask Consistency Regularization (MCR), a novel training strategy designed specifically for object removal tasks. During training, our approach introduces two mask perturbations: dilation and reshape, enforcing consistency between the outputs of these perturbed branches and the original mask. The dilated masks help align the model's output with the surrounding content, while reshaped masks encourage the model to break the mask-shape bias. This combination of strategies enables MCR to produce more robust and contextually coherent inpainting results. Our experiments demonstrate that MCR significantly reduces hallucinations and mask-shape bias, leading to improved performance in object removal.
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。
