论文标题
基于注意的辅助激发显着对象检测
Attention-based Assisted Excitation for Salient Object Detection
论文作者
论文摘要
视觉注意力在各种应用中带来了卷积神经网络（CNN）的重大进展。在本文中，人类视觉皮层中基于对象的注意力激发了我们引入一种在CNN特征图中修改激活的机制。在此机制中，对象位置的激活在特征图中被激发。该机制特别受到大脑中基于对象的注意的基于注意力的增益调制的启发。它促进了视觉皮层中的图形隔离。与大脑类似，我们使用该想法来应对显着对象检测的两个挑战：收集对象内部零件，而从具有简洁边界的背景隔离。我们使用编码器部分中的不同体系结构（包括Alexnet，VGG和Resnet）在U-NET模型中实现了基于对象的注意。在三个基准数据集上检查了提出的方法：HKU-IS，MSRB和Pascal-S。实验结果表明，我们受启发的方法可以从平均绝对误差和F量表方面显着改善结果。结果还表明，我们提出的方法不仅可以更好地捕获边界，还可以捕获对象内部。因此，它可以应对上述挑战。
Visual attention brings significant progress for Convolution Neural Networks (CNNs) in various applications. In this paper, object-based attention in human visual cortex inspires us to introduce a mechanism for modification of activations in feature maps of CNNs. In this mechanism, the activations of object locations are excited in feature maps. This mechanism is specifically inspired by attention-based gain modulation in object-based attention in brain. It facilitates figure-ground segregation in the visual cortex. Similar to brain, we use the idea to address two challenges in salient object detection: gathering object interior parts while segregation from background with concise boundaries. We implement the object-based attention in the U-net model using different architectures in the encoder parts, including AlexNet, VGG, and ResNet. The proposed method was examined on three benchmark datasets: HKU-IS, MSRB, and PASCAL-S. Experimental results showed that our inspired method could significantly improve the results in terms of mean absolute error and F-measure. The results also showed that our proposed method better captured not only the boundary but also the object interior. Thus, it can tackle the mentioned challenges.
