# Contour Loss: Boundary-Aware Learning for Salient Object Segmentation

arXiv: https://arxiv.org/abs/1908.01975 · HTML: https://mlsanity.com/paper/1908.01975/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [GlassNet: Label Decoupling-based Three-stream Neural Network for Robust Image Glass Detection](https://mlsanity.com/paper/2108.11117/) (2021)
- [Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark](https://mlsanity.com/paper/2112.01019/) (2021)
- [An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images](https://mlsanity.com/paper/2204.01341/) (2022)
- [Activation to Saliency: Forming High-Quality Labels for Completely Unsupervised Salient Object Detection](https://mlsanity.com/paper/2112.03650/) (2021)
- [A Novel Shape Guided Transformer Network for Instance Segmentation in Remote Sensing Images](https://mlsanity.com/paper/2501.00360/) (2024)

## Abstract (arXiv)

We present a learning model that makes full use of boundary information for salient object segmentation. Specifically, we come up with a novel loss function, i.e., Contour Loss, which leverages object contours to guide models to perceive salient object boundaries. Such a boundary-aware network can learn boundary-wise distinctions between salient objects and background, hence effectively facilitating the saliency detection. Yet the Contour Loss emphasizes on the local saliency. We further propose the hierarchical global attention module (HGAM), which forces the model hierarchically attend to global contexts, thus captures the global visual saliency. Comprehensive experiments on six benchmark datasets show that our method achieves superior performance over state-of-the-art ones. Moreover, our model has a real-time speed of 26 fps on a TITAN X GPU.