# An End-to-End Network for Co-Saliency Detection in One Single Image

arXiv: https://arxiv.org/abs/1910.11819 · HTML: https://mlsanity.com/paper/1910.11819/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [Free Lunch for Co-Saliency Detection: Context Adjustment](https://mlsanity.com/paper/2108.02093/) (2021)
- [Line Drawing Guided Progressive Inpainting of Mural Damage](https://mlsanity.com/paper/2211.06649/) (2022)
- [Towards Imbalanced Motion: Part-Decoupling Network for Video Portrait Segmentation](https://mlsanity.com/paper/2307.16565/) (2023)

## Abstract (arXiv)

Co-saliency detection within a single image is a common vision problem that has received little attention and has not yet been well addressed. Existing methods often used a bottom-up strategy to infer co-saliency in an image in which salient regions are firstly detected using visual primitives such as color and shape and then grouped and merged into a co-saliency map. However, co-saliency is intrinsically perceived complexly with bottom-up and top-down strategies combined in human vision. To address this problem, this study proposes a novel end-to-end trainable network comprising a backbone net and two branch nets. The backbone net uses ground-truth masks as top-down guidance for saliency prediction, whereas the two branch nets construct triplet proposals for regional feature mapping and clustering, which drives the network to be bottom-up sensitive to co-salient regions. We construct a new dataset of 2,019 natural images with co-saliency in each image to evaluate the proposed method. Experimental results show that the proposed method achieves state-of-the-art accuracy with a running speed of 28 fps.