# Comprehensive Saliency Fusion for Object Co-segmentation

arXiv: https://arxiv.org/abs/2201.12828 · HTML: https://mlsanity.com/paper/2201.12828/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 30 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [EGNet:Edge Guidance Network for Salient Object Detection](https://mlsanity.com/paper/1908.08297/) (2019)
- [A Simple Pooling-Based Design for Real-Time Salient Object Detection](https://mlsanity.com/paper/1904.09569/) (2019)
- [DGC-Net: Dense Geometric Correspondence Network](https://mlsanity.com/paper/1810.08393/) (2018)
- [Deep Object Co-Segmentation](https://mlsanity.com/paper/1804.06423/) (2018)
- [Semantic Aware Attention Based Deep Object Co-segmentation](https://mlsanity.com/paper/1810.06859/) (2018)
- [Deep Object Co-segmentation via Spatial-Semantic Network Modulation](https://mlsanity.com/paper/1911.12950/) (2019)
- [Image Co-skeletonization via Co-segmentation](https://mlsanity.com/paper/2004.05575/) (2020)

## Abstract (arXiv)

Object co-segmentation has drawn significant attention in recent years, thanks to its clarity on the expected foreground, the shared object in a group of images. Saliency fusion has been one of the promising ways to carry it out. However, prior works either fuse saliency maps of the same image or saliency maps of different images to extract the expected foregrounds. Also, they rely on hand-crafted saliency extraction and correspondence processes in most cases. This paper revisits the problem and proposes fusing saliency maps of both the same image and different images. It also leverages advances in deep learning for the saliency extraction and correspondence processes. Hence, we call it comprehensive saliency fusion. Our experiments reveal that our approach achieves much-improved object co-segmentation results compared to prior works on important benchmark datasets such as iCoseg, MSRC, and Internet Images.