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.
#3 best model for Visual Object Tracking on YouTube-VOS
We propose a method for semi-supervised semantic segmentation using an adversarial network.
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.
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.
We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations.
The ability to understand visual information from limited labeled data is an important aspect of machine learning.