Semi-supervised semantic segmentation is the task of doing semantic segmentation in a semi-supervised way.
( Image credit: AdaptSegNet )
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.
Weakly supervised semantic segmentation (WSSS) using only image-level labels can greatly reduce the annotation cost and therefore has attracted considerable research interest.