# Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

arXiv: https://arxiv.org/abs/1608.05177 · HTML: https://mlsanity.com/paper/1608.05177/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [Benchmarking Deep Models for Salient Object Detection](https://mlsanity.com/paper/2202.02925/) (2022)
- [Activation to Saliency: Forming High-Quality Labels for Completely Unsupervised Salient Object Detection](https://mlsanity.com/paper/2112.03650/) (2021)

## Abstract (arXiv)

This paper proposes a novel saliency detection method by developing a deeply-supervised recurrent convolutional neural network (DSRCNN), which performs a full image-to-image saliency prediction. For saliency detection, the local, global, and contextual information of salient objects is important to obtain a high quality salient map. To achieve this goal, the DSRCNN is designed based on VGGNet-16. Firstly, the recurrent connections are incorporated into each convolutional layer, which can make the model more powerful for learning the contextual information. Secondly, side-output layers are added to conduct the deeply-supervised operation, which can make the model learn more discriminative and robust features by effecting the intermediate layers. Finally, all of the side-outputs are fused to integrate the local and global information to get the final saliency detection results. Therefore, the DSRCNN combines the advantages of recurrent convolutional neural networks and deeply-supervised nets. The DSRCNN model is tested on five benchmark datasets, and experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art saliency detection approaches on all test datasets.