GOSS: towards generalized open-set semantic segmentation
Jie Hong, Weihao Li, Junlin Han, Jiyang Zheng, Pengfei Fang, Mehrtash Harandi, Lars Petersson
Abstract
Abstract In this paper, we extend Open-set Semantic Segmentation (OSS) into a new image segmentation task called Generalized Open-set Semantic Segmentation (GOSS). Previously, with well-known OSS, the intelligent agents only detect unknown regions without further processing, limiting their perception capacity of the environment. It stands to reason that further analysis of the detected unknown pixels would be beneficial for agents’ decision-making. Therefore, we propose GOSS, which holistically unifies the abilities of two well-defined segmentation tasks, i.e. OSS and generic segmentation. Specifically, GOSS classifies pixels as belonging to known classes, and clusters (or groups) of pixels of unknown class are labelled as such. We propose a metric that balances the pixel classification and clustering aspects to evaluate this newly expanded task. Moreover, we build benchmark tests on existing datasets and propose neural architectures as baselines. Our experiments on multiple benchmarks demonstrate the effectiveness of our baselines. Code is made available at https://github.com/JHome1/GOSS_Segmentor .