42 citations · 42 across the 1 of their papers we have counts for
5 papers
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…
Class-Weighted Convolutional Features for Visual Instance Search
Albert Jimenez, Jose M. Alvarez, Xavier Giro-i-Nieto
Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the da…
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…
DecomposeMe: Simplifying ConvNets for End-to-End Learning
Jose Alvarez, Lars Petersson
Deep learning and convolutional neural networks (ConvNets) have been successfully applied to most relevant tasks in the computer vision community. However, these networks are compu…
Learning Image Matching by Simply Watching Video
Gucan Long, Laurent Kneip, Jose M. Alvarez +1
This work presents an unsupervised learning based approach to the ubiquitous computer vision problem of image matching. We start from the insight that the problem of frame-interpol…