论文标题
梯度诱导的联合检测
Gradient-Induced Co-Saliency Detection
论文作者
论文摘要
共同检测（共同传播）旨在将一组相关图像中的共同显着前景分割。在本文中，受到人类行为的启发，我们提出了梯度诱导的共同检测方法（GICD）方法。我们首先将嵌入空间中分组图像的共识表示。然后，通过将单个图像与共识表示形式进行比较，我们利用反馈梯度信息来引起更多关注歧视性的共同特征。此外，由于缺乏共同编号培训数据，我们设计了一种拼图训练策略，可以通过该策略在没有额外像素级注释的情况下在一般显着性数据集上培训共同编号网络。为了评估共同方法在多个前景之间发现共同对象的性能的性能，我们构建了一个具有挑战性的可口数据集，其中每个图像与共同升级对象一起至少包含一个外部前景。实验表明，我们的GICD实现了最先进的表现。我们的代码和数据集可在https://mmcheng.net/gicd/上找到。
Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images. In this paper, inspired by human behavior, we propose a gradient-induced co-saliency detection (GICD) method. We first abstract a consensus representation for the grouped images in the embedding space; then, by comparing the single image with consensus representation, we utilize the feedback gradient information to induce more attention to the discriminative co-salient features. In addition, due to the lack of Co-SOD training data, we design a jigsaw training strategy, with which Co-SOD networks can be trained on general saliency datasets without extra pixel-level annotations. To evaluate the performance of Co-SOD methods on discovering the co-salient object among multiple foregrounds, we construct a challenging CoCA dataset, where each image contains at least one extraneous foreground along with the co-salient object. Experiments demonstrate that our GICD achieves state-of-the-art performance. Our codes and dataset are available at https://mmcheng.net/gicd/.
