无需训练即可精准移除图像物体，动态调节注意力提升修复质量。
AdaEraser: Training-Free Object Removal via Adaptive Attention Suppression
- 根据目标物体存在概率，动态调整自注意力抑制强度。
- 在去噪过程中逐步感知移除效果，避免背景重建质量下降。
- 性能超越部分训练型方法，适合图像编辑与内容净化场景。
物体移除旨在消除图像中的指定物体，并用背景内容合理填补受影响区域。现有无训练方法通常在生成过程中屏蔽自注意力层中物体区域的注意力，利用周围背景信息恢复图像。但对空缺区域的盲目注意力抑制会降低生成质量，因模型需同时重建这些区域的背景内容。为此，我们提出AdaEraser，一种基于目标物体概念存在概率估计的自适应框架。通过分析去噪步骤前后自注意力图的变化，我们设计了逐标记的自适应注意力抑制策略，使物体移除在去噪过程中实现渐进式感知，自注意力层中的抑制强度可动态调整。大量实验表明，AdaEraser在物体移除任务上表现优异，甚至优于部分训练型方法。
原文摘要 · Abstract (English)
Object removal aims to eliminate specified objects from images while plausibly inpainting the affected regions with background content. Current training-free methods typically block attention to object regions within self-attention layers during the image generation process, leveraging surrounding background information to restore the image. However, indiscriminate suppression of self-attention in the vacated areas can degrade generation quality, as the model must simultaneously reconstruct background content in these regions. To solve this conflict, we propose AdaEraser, an adaptive framework that dynamically modulates attention based on the estimated presence of target object concepts. Through analysis of self-attention map evolution across denoising timesteps before and during removal, we develop a token-wise adaptive attention suppression strategy. This approach enables progressive perception of object removal throughout the denoising process, with the suppression strength in self-attention layers adjusted adaptively. Extensive experiments demonstrate that AdaEraser achieves superior performance in object removal, outperforming even training-based methods.
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