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BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks by Pham, Viet-Quoc; Ito, Satoshi; Kozakaya, Tatsuo is a scholarly article available to read on EtoBox.

What is BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks about?

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.

Author

Pham, Viet-Quoc; Ito, Satoshi; Kozakaya, Tatsuo

Published

2017

Language

EN