429 citations · 450 across the 13 of their papers we have counts for
9 papers · 1 filter
Commands 4 Autonomous Vehicles (C4AV) Workshop Summary
Thierry Deruyttere, Simon Vandenhende, Dusan Grujicic +5
The task of visual grounding requires locating the most relevant region or object in an image, given a natural language query. So far, progress on this task was mostly measured on…
AOWS: Adaptive and optimal network width search with latency constraints
Maxim Berman, Leonid Pishchulin, Ning Xu +2
Neural architecture search (NAS) approaches aim at automatically finding novel CNN architectures that fit computational constraints while maintaining a good performance on the targ…
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice
Jeroen Bertels, Tom Eelbode, Maxim Berman +4
The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation…
Adaptive Compression-based Lifelong Learning
Shivangi Srivastava, Maxim Berman, Matthew B. Blaschko +1
The problem of a deep learning model losing performance on a previously learned task when fine-tuned to a new one is a phenomenon known as Catastrophic forgetting. There are two ma…
Generating superpixels using deep image representations
Thomas Verelst, Matthew Blaschko, Maxim Berman
Superpixel algorithms are a common pre-processing step for computer vision algorithms such as segmentation, object tracking and localization. Many superpixel methods only rely on c…
Efficient semantic image segmentation with superpixel pooling
Mathijs Schuurmans, Maxim Berman, Matthew B. Blaschko
In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement f…