Gradient-Induced Co-Saliency Detection

• URL: http://arxiv.org/abs/2004.13364v3
• Date: Sat, 12 Dec 2020 08:03:45 GMT
• Title: Gradient-Induced Co-Saliency Detection
• Authors: Zhao Zhang, Wenda Jin, Jun Xu, Ming-Ming Cheng
• Abstract summary: 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 method.
• Score: 81.54194063218216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

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