Semi-supervised semantic segmentation is the task of doing semantic segmentation in a semi-supervised way.
In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach.
SOTA for Visual Object Tracking on VOT2017/18
We propose a method for semi-supervised semantic segmentation using an adversarial network.
We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations.
In addition, our work presents a comprehensive analysis of different GAN architectures for semi-supervised segmentation, showing recent techniques like feature matching to yield a higher performance than conventional adversarial training approaches.