64 citations · 343 across the 45 of their papers we have counts for
8 papers · 1 filter
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…
Deep Subspace Clustering Networks
Pan Ji, Tong Zhang, Hongdong Li +2
We present a novel deep neural network architecture for unsupervised subspace clustering. This architecture is built upon deep auto-encoders, which non-linearly map the input data…
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation
Fatemeh Sadat Saleh, Mohammad Sadegh Aliakbarian, Mathieu Salzmann +2
Pixel-level annotations are expensive and time-consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recent y…
Imposing Hard Constraints on Deep Networks: Promises and Limitations
Pablo Márquez-Neila, Mathieu Salzmann, Pascal Fua
Imposing constraints on the output of a Deep Neural Net is one way to improve the quality of its predictions while loosening the requirements for labeled training data. Such constr…
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation
Fatemeh Sadat Saleh, Mohammad Sadegh Aliakbarian, Mathieu Salzmann +3
Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently…