913 citations · 2.7k across the 51 of their papers we have counts for
5 papers · 1 filter
Compression-aware Training of Deep Networks
Jose M. Alvarez, Mathieu Salzmann
In recent years, great progress has been made in a variety of application domains thanks to the development of increasingly deeper neural networks. Unfortunately, the huge number o…
Domain-adaptive deep network compression
Marc Masana, Joost van de Weijer, Luis Herranz +2
Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that…
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…