  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/.
