no code implementations • CVPR 2021 • Haoyu Ma, Xiangru Lin, Zifeng Wu, Yizhou Yu
Unsupervised domain adaptation (UDA) in semantic segmentation is a fundamental yet promising task relieving the need for laborious annotation works.
Ranked #22 on Synthetic-to-Real Translation on SYNTHIA-to-Cityscapes
no code implementations • 8 Oct 2019 • José Ignacio Orlando, Huazhu Fu, João Barbossa Breda, Karel van Keer, Deepti. R. Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, Joonho Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R. Phaye, Sharath M. Shankaranarayana, Apoorva Sikka, Jaemin Son, Anton Van Den Hengel, Shujun Wang, Junyan Wu, Zifeng Wu, Guanghui Xu, Yongli Xu, Pengshuai Yin, Fei Li, Yanwu Xu, Xiulan Zhang, Hrvoje Bogunović
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.
no code implementations • 1 Dec 2017 • 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.
3 code implementations • 30 Nov 2016 • Zifeng Wu, Chunhua Shen, Anton Van Den Hengel
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
no code implementations • 23 May 2016 • Zifeng Wu, Chunhua Shen, Anton Van Den Hengel
(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 #52 on Semantic Segmentation on PASCAL Context
no code implementations • 8 May 2016 • Yuanzhouhan Cao, Zifeng Wu, Chunhua Shen
Then we train fully convolutional deep residual networks to predict the depth label of each pixel.
no code implementations • 15 Apr 2016 • Zifeng Wu, Chunhua Shen, Anton Van Den Hengel
(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.