ML Anthology

Co-Saliency Detection via Mask-Guided Fully Convolutional Networks with Multi-Scale Label Smoothing

Kaihua Zhang, Tengpeng Li, Bo Liu, Qingshan Liu
CVPR 2019
doi:10.1109/CVPR.2019.00321 /cvpr/2019/zhang2019cvpr-cosaliency/

Abstract

In image co-saliency detection problem, one critical issue is how to model the concurrent pattern of the co-salient parts, which appears both within each image and across all the relevant images. In this paper, we propose a hierarchical image co-saliency detection framework as a coarse to fine strategy to capture this pattern. We first propose a mask-guided fully convolutional network structure to generate the initial co-saliency detection result. The mask is used for background removal and it is learned from the high-level feature response maps of the pre-trained VGG-net output. We next propose a multi-scale label smoothing model to further refine the detection result. The proposed model jointly optimizes the label smoothness of pixels and superpixels. Experiment results on three popular image co-saliency detection benchmark datasets including iCoseg, MSRC and Cosal2015 demonstrate the remarkable performance compared with the state-of-the-art methods.

PDF CVPR Semantic Scholar

Cite

Text

Zhang et al. "Co-Saliency Detection via Mask-Guided Fully Convolutional Networks with Multi-Scale Label Smoothing." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00321

Markdown

[Zhang et al. "Co-Saliency Detection via Mask-Guided Fully Convolutional Networks with Multi-Scale Label Smoothing." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/zhang2019cvpr-cosaliency/) doi:10.1109/CVPR.2019.00321

BibTeX

@inproceedings{zhang2019cvpr-cosaliency,
  title     = {{Co-Saliency Detection via Mask-Guided Fully Convolutional Networks with Multi-Scale Label Smoothing}},
  author    = {Zhang, Kaihua and Li, Tengpeng and Liu, Bo and Liu, Qingshan},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00321},
  url       = {https://mlanthology.org/cvpr/2019/zhang2019cvpr-cosaliency/}
}