# Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss

arXiv: https://arxiv.org/abs/1901.06823 · HTML: https://mlsanity.com/paper/1901.06823/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2019.

## Built on by (most cited first, 6 of 6)

- [Salient Object Detection via Integrity Learning](https://mlsanity.com/paper/2101.07663/) (2021)
- [Multi-Scale and Detail-Enhanced Segment Anything Model for Salient Object Detection](https://mlsanity.com/paper/2408.04326/) (2024)
- [Salient Objects in Clutter](https://mlsanity.com/paper/2105.03053/) (2021)
- [MAS-SAM: Segment Any Marine Animal with Aggregated Features](https://mlsanity.com/paper/2404.15700/) (2024)
- [Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection](https://mlsanity.com/paper/2412.16840/) (2024)
- [Learning Video Salient Object Detection Progressively from Unlabeled Videos](https://mlsanity.com/paper/2204.02008/) (2022)

## Abstract (arXiv)

Salient object detection (SOD), which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to effectively learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new weighted structural loss function to ensure clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods with a large margin.