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Found 7 papers, 1 papers with code
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works.
Ranked #23 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one.
Real-time Semantic Image Segmentation via Spatial Sparsity
We propose an approach to semantic (image) segmentation that reduces the computational costs by a factor of 25 with limited impact on the quality of results.
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
As a result, we are able to derive a new, shallower, architecture of residual networks which significantly outperforms much deeper models such as ResNet-200 on the ImageNet classification dataset.
Ranked #11 on
Semantic Segmentation
on PASCAL VOC 2012 test
Bridging Category-level and Instance-level Semantic Image Segmentation
(iii) As the performance of semantic category segmentation has a significant impact on the instance-level segmentation, which is the second step of our approach, we train fully convolutional residual networks to achieve the best semantic category segmentation accuracy.
Ranked #53 on
Semantic Segmentation
on PASCAL Context
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks
Then we train fully convolutional deep residual networks to predict the depth label of each pixel.
High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks
(i) First, we evaluate different variations of a fully convolutional residual network so as to find the best configuration, including the number of layers, the resolution of feature maps, and the size of field-of-view.
