PEEKABOO：隐藏图像部分区域以进行无监督目标定位
PEEKABOO: Hiding parts of an image for unsupervised object localization
AI总结：
提出单阶段框架PEEKABOO，通过图像掩码在像素和形状级学习上下文表示，无需显式监督即可推断目标位置，在无监督目标定位任务中表现优异。
摘要：
由于缺乏关键视觉信息（如目标的外观、类型和数量），以及在监督设置中通常可用的带标签目标类别，以无监督方式进行目标定位面临重大挑战。尽管近期无监督目标定位方法通过利用自监督视觉表示取得了显著进展，但它们通常需要计算密集的训练过程，导致在计算、可学习参数和数据方面资源需求较高。它们还缺乏对视觉上下文的显式建模，可能限制了目标定位的准确性。为应对这些挑战，我们提出了一个单阶段学习框架PEEKABOO，通过图像掩码在像素级和形状级学习基于上下文的表示，实现无监督目标定位。其核心思想是选择性地隐藏图像的某些部分，并利用剩余图像信息推断目标位置，无需显式监督。在多个基准数据集上的定量和定性实验结果表明，与最先进方法相比，该方法在单目标发现和无监督显著目标检测任务中具有简洁性、有效性和竞争力。代码和预训练模型可在 https://github.com/hasibzunair/peekaboo 获取。
英文摘要：
Localizing objects in an unsupervised manner poses significant challenges due to the absence of key visual information such as the appearance, type and number of objects, as well as the lack of labeled object classes typically available in supervised settings. While recent approaches to unsupervised object localization have demonstrated significant progress by leveraging self-supervised visual representations, they often require computationally intensive training processes, resulting in high resource demands in terms of computation, learnable parameters, and data. They also lack explicit modeling of visual context, potentially limiting their accuracy in object localization. To tackle these challenges, we propose a single-stage learning framework, dubbed PEEKABOO, for unsupervised object localization by learning context-based representations at both the pixel- and shape-level of the localized objects through image masking. The key idea is to selectively hide parts of an image and leverage the remaining image information to infer the location of objects without explicit supervision. The experimental results, both quantitative and qualitative, across various benchmark datasets, demonstrate the simplicity, effectiveness and competitive performance of our approach compared to state-of-the-art methods in both single object discovery and unsupervised salient object detection tasks. Code and pre-trained models are available at: https://github.com/hasibzunair/peekaboo
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。
