Semi-Supervised Semantic Segmentation

31 papers with code • 17 benchmarks • 2 datasets

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Greatest papers with code

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.

Real-Time Visual Tracking Semi-Supervised Semantic Segmentation +2

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning

arnab39/FewShot_GAN-Unet3D 29 Oct 2018

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.

3D Medical Imaging Segmentation Brain Image Segmentation +3

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.

Semi-Supervised Semantic Segmentation

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

TheLegendAli/DeepLab-Context 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.

Semi-Supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation

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.

Semi-Supervised Semantic Segmentation

Guided Collaborative Training for Pixel-wise Semi-Supervised Learning

ZHKKKe/PixelSSL ECCV 2020

Although SSL methods have achieved impressive results in image classification, the performances of applying them to pixel-wise tasks are unsatisfactory due to their need for dense outputs.

Image Denoising Image Enhancement +2

Semi-supervised semantic segmentation needs strong, varied perturbations

ZHKKKe/PixelSSL 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.

General Classification Semi-Supervised Semantic Segmentation

Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation

lhoyer/improving_segmentation_with_selfsupervised_depth 28 Aug 2021

Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process.

Data Augmentation Domain Adaptation +3

Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation

lhoyer/improving_segmentation_with_selfsupervised_depth CVPR 2021

Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process.

Data Augmentation Monocular Depth Estimation +1