Semi-supervised semantic segmentation is the task of doing semantic segmentation in a semi-supervised way.
( Image credit: AdaptSegNet )
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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.
#3 best model for Semi-Supervised Semantic Segmentation on Cityscapes 12.5% labeled
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.
This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region.
The major challenge of this task lies in how to exploit unlabeled data efficiently and thoroughly.
Weakly supervised semantic segmentation (WSSS) using only image-level labels can greatly reduce the annotation cost and therefore has attracted considerable research interest.