# BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1706.02135 · HTML: https://mlsanity.com/paper/1706.02135/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 1 of 1)

- [Contour Loss for Instance Segmentation via k-step Distance Transformation Image](https://mlsanity.com/paper/2102.10854/) (2021)

## Abstract (arXiv)

We present a simple and effective framework for simultaneous semantic segmentation and instance segmentation with Fully Convolutional Networks (FCNs). The method, called BiSeg, predicts instance segmentation as a posterior in Bayesian inference, where semantic segmentation is used as a prior. We extend the idea of position-sensitive score maps used in recent methods to a fusion of multiple score maps at different scales and partition modes, and adopt it as a robust likelihood for instance segmentation inference. As both Bayesian inference and map fusion are performed per pixel, BiSeg is a fully convolutional end-to-end solution that inherits all the advantages of FCNs. We demonstrate state-of-the-art instance segmentation accuracy on PASCAL VOC.