Abstract
Extreme amodal detection is the task of inferring the 2D location of objects that are not fully visible in the input image but are visible within an expanded field-of-view. This differs from amodal detection, where the object is partially visible within the input image, but is occluded. In this paper, we consider the sub-problem of face detection, since this class provides motivating applications involving safety and privacy, but do not tailor our method specifically to this class. Existing approaches rely on image sequences so that missing detections may be interpolated from surrounding frames or make use of generative models to sample possible completions. In contrast, we consider the single-image task and propose a more efficient, sample-free approach that makes use of the contextual cues from the image to infer the presence of unseen faces. We design a heatmap-based extreme amodal object detector that addresses the problem of efficiently predicting a lot (the out-of-frame region) from a little (the image) with a selective coarse-to-fine decoder. Our method establishes strong results for this new task, even outperforming less efficient generative approaches.
Abstract (translated)
极端模态检测（Extreme Amodal Detection）的任务是推断出输入图像中未完全可见但可以在扩展视野范围内看到的对象的二维位置。这与一般的模态检测不同，后者是指对象在输入图像中部分可见但被遮挡的情况。本文着重探讨了面部检测这一子问题，因为此类应用涉及安全和隐私领域的重要场景，但我们并未将方法专门针对这个类别进行设计。现有的方法依赖于图序列，以便从周围的帧中插值缺失的检测或利用生成模型来采样可能的完成情况。相比之下，我们考虑单一图像的任务，并提出了一种更高效的无需样本的方法，该方法利用来自图像的情境线索来推断未见人脸的存在。 为了应对这个任务，我们设计了一个基于热图（heatmap）的极端模态对象检测器，它解决了从少量信息高效预测大量数据的问题（即预测超出画面范围的部分），通过选择性地采用粗到细的解码器。我们的方法在这一新任务上取得了显著成果，并且优于其他更不高效的生成式方法。
URL
https://arxiv.org/abs/2510.06791
