Abstract
Object removal differs from common inpainting, since it must prevent the masked target from reappearing and reconstruct the occluded background with structural and contextual fidelity, rather than merely filling a hole plausibly. Recent dataset-free approaches that redirect self-attention inside the mask fail in two ways: non-target foregrounds are often misinterpreted as background, which regenerates unwanted objects, and direct attention manipulation disrupts fine details and hinders coherent integration of background cues. We propose EraseLoRA, a novel dataset-free framework that replaces attention surgery with background-aware reasoning and test-time adaptation. First, Background-aware Foreground Exclusion (BFE), uses a multimodal large-language models to separate target foreground, non-target foregrounds, and clean background from a single image-mask pair without paired supervision, producing reliable background cues while excluding distractors. Second, Background-aware Reconstruction with Subtype Aggregation (BRSA), performs test-time optimization that treats inferred background subtypes as complementary pieces and enforces their consistent integration through reconstruction and alignment objectives, preserving local detail and global structure without explicit attention intervention. We validate EraseLoRA as a plug-in to pretrained diffusion models and across benchmarks for object removal, demonstrating consistent improvements over dataset-free baselines and competitive results against dataset-driven methods. The code will be made available upon publication.
Abstract (translated)
对象移除与常见的图像修复（inpainting）有所不同，因为它必须防止被遮罩的目标重新出现，并且需要以结构和上下文的一致性来重建被遮挡的背景，而不仅仅是合理地填充一个空洞。最近一些不依赖数据集的方法通过重定向掩码内的自注意力机制来处理对象移除问题，在两个方面存在不足：非目标前景经常被误认为是背景，从而生成不需要的对象；直接操作注意机制会干扰细节，并阻碍背景线索的连贯整合。 我们提出了一种新的无数据集框架——EraseLoRA，它用基于背景感知推理和测试时适应的方法替代了注意力手术。首先，通过“基于背景感知的前景排除”（BFE），利用多模态大型语言模型从单一图像-掩码对中分离出目标前景、非目标前景以及干净背景，无需配对监督即可产生可靠的背景线索并排除干扰项。其次，“基于背景感知的子类型聚合重建”（BRSA）执行测试时优化，将推断出来的背景子类视为互补部分，并通过重构和对齐目标强制它们的一致整合，在没有显式注意力干预的情况下保持局部细节和全局结构。 我们在预训练扩散模型中验证了EraseLoRA作为插件的有效性，并在对象移除的基准数据集上进行了测试，结果表明相比于无数据集的基础方法它有持续改进的表现，同时也与基于数据驱动的方法具有竞争力。代码将在发表时提供。
URL
https://arxiv.org/abs/2512.21545
