Semi-Supervised Semantic Segmentation

90 papers with code • 45 benchmarks • 13 datasets

Models that are trained with a small number of labeled examples and a large number of unlabeled examples and whose aim is to learn to segment an image (i.e. assign a class to every pixel).

Libraries

Use these libraries to find Semi-Supervised Semantic Segmentation models and implementations
3 papers
2,046
2 papers
33

Most implemented papers

Adversarial Learning for Semi-Supervised Semantic Segmentation

hfslyc/AdvSemiSeg ICLR 2018

We propose a method for semi-supervised semantic segmentation using an adversarial network.

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

CuriousAI/mean-teacher NeurIPS 2017

Without changing the network architecture, Mean Teacher achieves an error rate of 4. 35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels.

Semi-supervised semantic segmentation needs strong, varied perturbations

Britefury/cutmix-semisup-seg 5 Jun 2019

We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success.

Semi-Supervised Semantic Segmentation with Cross-Consistency Training

yassouali/CCT CVPR 2020

To leverage the unlabeled examples, we enforce a consistency between the main decoder predictions and those of the auxiliary decoders, taking as inputs different perturbed versions of the encoder's output, and consequently, improving the encoder's representations.

Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

deeplab/deeplab-public 9 Feb 2015

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.

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

HyeonwooNoh/caffe NeurIPS 2015

We propose a novel deep neural network architecture for semi-supervised semantic segmentation using heterogeneous annotations.

Fast Online Object Tracking and Segmentation: A Unifying Approach

foolwood/SiamMask CVPR 2019

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.

Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

charlesCXK/TorchSemiSeg CVPR 2021

Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image.

Part-aware Prototype Network for Few-shot Semantic Segmentation

Xiangyi1996/PPNet-PyTorch ECCV 2020

In this paper, we propose a novel few-shot semantic segmentation framework based on the prototype representation.

ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning

WilhelmT/ClassMix 15 Jul 2020

A key challenge is that common augmentations used in semi-supervised classification are less effective for semantic segmentation.