858 citations · 1.4k across the 7 of their papers we have counts for
7 papers
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization
Haoyu Ma, Xiangru Lin, Zifeng Wu +1
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works. However, the domain shifts/disc…
REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
José Ignacio Orlando, Huazhu Fu, João Barbossa Breda +28
Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modali…
Real-time Semantic Image Segmentation via Spatial Sparsity
Zifeng Wu, Chunhua Shen, Anton van den Hengel
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. Semantic segmentation…
Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
Zifeng Wu, Chunhua Shen, Anton van den Hengel
The trend towards increasingly deep neural networks has been driven by a general observation that increasing depth increases the performance of a network. Recently, however, eviden…
Bridging Category-level and Instance-level Semantic Image Segmentation
Zifeng Wu, Chunhua Shen, Anton van den Hengel
We propose an approach to instance-level image segmentation that is built on top of category-level segmentation. Specifically, for each pixel in a semantic category mask, its corre…
Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks
Yuanzhouhan Cao, Zifeng Wu, Chunhua Shen
Depth estimation from single monocular images is a key component of scene understanding and has benefited largely from deep convolutional neural networks (CNN) recently. In this ar…
High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks
Zifeng Wu, Chunhua Shen, Anton van den Hengel
We propose a method for high-performance semantic image segmentation (or semantic pixel labelling) based on very deep residual networks, which achieves the state-of-the-art perform…