Semi-Supervised Semantic Segmentation

55 papers with code • 26 benchmarks • 6 datasets

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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 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.

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.

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.

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 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.

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

xinge008/Cylinder3D CVPR 2021

However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited.

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.