# SAC-Net: Spatial Attenuation Context for Salient Object Detection

arXiv: https://arxiv.org/abs/1903.10152 · HTML: https://mlsanity.com/paper/1903.10152/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 15 of 15)

- [SwinNet: Swin Transformer drives edge-aware RGB-D and RGB-T salient object detection](https://mlsanity.com/paper/2204.05585/) (2022)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Boundary-Aware Segmentation Network for Mobile and Web Applications](https://mlsanity.com/paper/2101.04704/) (2021)
- [Deep Texture-Aware Features for Camouflaged Object Detection](https://mlsanity.com/paper/2102.02996/) (2021)
- [HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection](https://mlsanity.com/paper/2301.03036/) (2023)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [Unifying Global-Local Representations in Salient Object Detection with Transformer](https://mlsanity.com/paper/2108.02759/) (2021)
- [Boosting Salient Object Detection with Transformer-based Asymmetric Bilateral U-Net](https://mlsanity.com/paper/2108.07851/) (2021)
- [Weakly-Supervised Saliency Detection via Salient Object Subitizing](https://mlsanity.com/paper/2101.00932/) (2021)
- [Semi-supervised Salient Object Detection with Effective Confidence Estimation](https://mlsanity.com/paper/2112.14019/) (2021)
- [Model Calibration in Dense Classification with Adaptive Label Perturbation](https://mlsanity.com/paper/2307.13539/) (2023)
- [Salient Object Detection From Arbitrary Modalities](https://mlsanity.com/paper/2405.03352/) (2024)
- [LC3Net: Ladder context correlation complementary network for salient object detection](https://mlsanity.com/paper/2110.10869/) (2021)
- [SalFAU-Net: Saliency Fusion Attention U-Net for Salient Object Detection](https://mlsanity.com/paper/2405.02906/) (2024)
- [Activation to Saliency: Forming High-Quality Labels for Completely Unsupervised Salient Object Detection](https://mlsanity.com/paper/2112.03650/) (2021)

## Abstract (arXiv)

This paper presents a new deep neural network design for salient object detection by maximizing the integration of local and global image context within, around, and beyond the salient objects. Our key idea is to adaptively propagate and aggregate the image context features with variable attenuation over the entire feature maps. To achieve this, we design the spatial attenuation context (SAC) module to recurrently translate and aggregate the context features independently with different attenuation factors and then to attentively learn the weights to adaptively integrate the aggregated context features. By further embedding the module to process individual layers in a deep network, namely SAC-Net, we can train the network end-to-end and optimize the context features for detecting salient objects. Compared with 29 state-of-the-art methods, experimental results show that our method performs favorably over all the others on six common benchmark data, both quantitatively and visually.