论文标题
与共同注意完全卷积网络的共同检测
Co-Saliency Detection with Co-Attention Fully Convolutional Network
论文作者
论文摘要
共同检测旨在从一组相关图像中检测常见的显着物体。已经通过完全卷积网络（FCN）框架进行了一些尝试，并获得了令人满意的检测结果。但是，由于堆叠卷积层和合并操作，边界细节往往会丢失。此外，现有模型经常在没有歧视的情况下使用提取的功能，从而导致表示的冗余，因为实际上并非所有功能都对最终预测有帮助，有些人甚至会分心。在本文中，我们提出了一个嵌入的FCN框架的共发模块，称为共同注意力FCN（CA-FCN）。具体而言，共发项模块被插入FCN的高级卷积层中，该层可以在常见的显着物体上分配更大的注意力权重，而背景上的较小对象则可以分配较小的对象，并在较小的物体上和罕见的干扰器分配了最终检测性能。在三个流行的共同基准数据集上进行的广泛实验证明了拟议的CA-FCN的优越性，在大多数情况下，这表现优于最先进的。此外，我们的新共同注意模块的有效性也通过消融研究验证。
Co-saliency detection aims to detect common salient objects from a group of relevant images. Some attempts have been made with the Fully Convolutional Network (FCN) framework and achieve satisfactory detection results. However, due to stacking convolution layers and pooling operation, the boundary details tend to be lost. In addition, existing models often utilize the extracted features without discrimination, leading to redundancy in representation since actually not all features are helpful to the final prediction and some even bring distraction. In this paper, we propose a co-attention module embedded FCN framework, called as Co-Attention FCN (CA-FCN). Specifically, the co-attention module is plugged into the high-level convolution layers of FCN, which can assign larger attention weights on the common salient objects and smaller ones on the background and uncommon distractors to boost final detection performance. Extensive experiments on three popular co-saliency benchmark datasets demonstrate the superiority of the proposed CA-FCN, which outperforms state-of-the-arts in most cases. Besides, the effectiveness of our new co-attention module is also validated with ablation studies.
