1.2k citations · 3k across the 74 of their papers we have counts for
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An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation
Jihan Yang, Ruijia Xu, Ruiyu Li +4
We focus on Unsupervised Domain Adaptation (UDA) for the task of semantic segmentation. Recently, adversarial alignment has been widely adopted to match the marginal distribution o…
ManiGAN: Text-Guided Image Manipulation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz +1
The goal of our paper is to semantically edit parts of an image matching a given text that describes desired attributes (e.g., texture, colour, and background), while preserving ot…
Domain-invariant Stereo Matching Networks
Feihu Zhang, Xiaojuan Qi, Ruigang Yang +3
State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast,…
Controllable Text-to-Image Generation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz +1
In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control part…
Improved Techniques for Training Adaptive Deep Networks
Hao Li, Hong Zhang, Xiaojuan Qi +2
Adaptive inference is a promising technique to improve the computational efficiency of deep models at test time. In contrast to static models which use the same computation graph f…
Human Pose Estimation with Spatial Contextual Information
Hong Zhang, Hao Ouyang, Shu Liu +4
We explore the importance of spatial contextual information in human pose estimation. Most state-of-the-art pose networks are trained in a multi-stage manner and produce several au…
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Christ, Hongwei Bran Li +106
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedi…