56 citations · 169 across the 9 of their papers we have counts for
16 papers
Self-supervised Training of Proposal-based Segmentation via Background Prediction
Isinsu Katircioglu, Helge Rhodin, Victor Constantin +3
While supervised object detection methods achieve impressive accuracy, they generalize poorly to images whose appearance significantly differs from the data they have been trained…
Backpropagation-Friendly Eigendecomposition
Wei Wang, Zheng Dang, Yinlin Hu +2
Eigendecomposition (ED) is widely used in deep networks. However, the backpropagation of its results tends to be numerically unstable, whether using ED directly or approximating it…
Recurrent U-Net for Resource-Constrained Segmentation
Wei Wang, Kaicheng Yu, Joachim Hugonot +2
State-of-the-art segmentation methods rely on very deep networks that are not always easy to train without very large training datasets and tend to be relatively slow to run on sta…
Detecting the Unexpected via Image Resynthesis
Krzysztof Lis, Krishna Nakka, Pascal Fua +1
Classical semantic segmentation methods, including the recent deep learning ones, assume that all classes observed at test time have been seen during training. In this paper, we ta…
Residual Parameter Transfer for Deep Domain Adaptation
Artem Rozantsev, Mathieu Salzmann, Pascal Fua
The goal of Deep Domain Adaptation is to make it possible to use Deep Nets trained in one domain where there is enough annotated training data in another where there is little or n…
Soft Correspondences in Multimodal Scene Parsing
Sarah Taghavi Namin, Mohammad Najafi, Mathieu Salzmann +1
Exploiting multiple modalities for semantic scene parsing has been shown to improve accuracy over the singlemodality scenario. However multimodal datasets often suffer from problem…