# BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks Source: [BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:43:17.087Z Authors: Viet-Quoc Pham, Satoshi Ito, Tatsuo Kozakaya Published: 2017-06-07T11:23:44.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()