801 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH39 papers
- Chinese University of Hong KongHK6 papers
- KU LeuvenBE6 papers
- Shenzhen Institutes of Advanced TechnologyCN5 papers
- Chinese Academy of SciencesCN4 papers
- Imperial College LondonGB3 papers
- Johns Hopkins UniversityUS3 papers
- Nanjing UniversityCN2 papers
- Technical University of MunichDE2 papers
- University of California, IrvineUS2 papers
- University of Chinese Academy of SciencesCN2 papers
- University of Electronic Science and Technology of ChinaCN2 papers
8 papers · 1 filter
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…
Transfer Learning via Unsupervised Task Discovery for Visual Question Answering
Hyeonwoo Noh, Taehoon Kim, Jonghwan Mun +1
We study how to leverage off-the-shelf visual and linguistic data to cope with out-of-vocabulary answers in visual question answering task. Existing large-scale visual datasets wit…
Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
Zheng Zhang, Li Liu, Jie Qin +5
How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-econom…
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
Jana Kemnitz, Christian F. Baumgartner, Wolfgang Wirth +3
Accurate segmentation of medical images is an important step towards analyzing and tracking disease related morphological alterations in the anatomy. Convolutional neural networks…
Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging
Xiaoran Chen, Nick Pawlowski, Martin Rajchl +2
Recent advances in deep learning led to novel generative modeling techniques that achieve unprecedented quality in generated samples and performance in learning complex distributio…
A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
Neerav Karani, Krishna Chaitanya, Christian Baumgartner +1
Convolutional neural networks (CNNs) have shown promising results on several segmentation tasks in magnetic resonance (MR) images. However, the accuracy of CNNs may degrade severel…
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
Yuhua Chen, Jordi Pont-Tuset, Alberto Montes +1
This paper tackles the problem of video object segmentation, given some user annotation which indicates the object of interest. The problem is formulated as pixel-wise retrieval in…
Learning the Synthesizability of Dynamic Texture Samples
Feng Yang, Gui-Song Xia, Dengxin Dai +1
A dynamic texture (DT) refers to a sequence of images that exhibit temporal regularities and has many applications in computer vision and graphics. Given an exemplar of dynamic tex…