Shadow detection is one of the most challenging issues in computer vision. Inspired by the great success of the convolutional neural network (CNN) for the problem of image restoration, learned features have been widely adopted for shadow detection. However, most existing methods still suffer from ambiguities driven by black-colored objects, which are not actually shaded, as well as the background clutter. In this letter, we propose the attentive feedback feature pyramid network (AFFPN) for shadow detection in a single image. The key idea of the proposed method is to extract shadow-relevant features based on multiple feedback modules, which are defined in the feature pyramid network. Specifically, attentive features extracted from each level of the encoder are progressively refined via connections between feedback modules from high-level to low-level layers for learning properties of shadow more accurately. Experimental results on benchmark datasets show that the proposed method is effective for shadow detection under complicated real-world environments. The code and model are publicly available at: https://github.com/JinheeKIM94/AFFPN_release.

中文翻译：

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用于阴影检测的注意力反馈特征金字塔网络


阴影检测是计算机视觉中最具挑战性的问题之一。受卷积神经网络（CNN）在图像恢复问题上取得巨大成功的启发，学习特征已被广泛用于阴影检测。然而，大多数现有方法仍然受到黑色物体（实际上没有阴影）以及背景杂乱造成的模糊性的影响。在这封信中，我们提出了用于单个图像中的阴影检测的注意力反馈特征金字塔网络（AFFPN）。该方法的关键思想是基于特征金字塔网络中定义的多个反馈模块来提取阴影相关特征。具体来说，从编码器的每个级别提取的注意力特征通过从高层到低层的反馈模块之间的连接逐步细化，以便更准确地学习阴影属性。基准数据集上的实验结果表明，该方法对于复杂现实环境下的阴影检测是有效的。代码和模型可在以下网址公开获取：https://github.com/JinheeKIM94/AFFPN_release。