13 citations · 13 across the 1 of their papers we have counts for
4 papers · 1 filter
FroDO: From Detections to 3D Objects
Kejie Li, Martin Rünz, Meng Tang +8
Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects.…
Constrained-CNN losses for weakly supervised segmentation
Hoel Kervadec, Jose Dolz, Meng Tang +3
Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need fo…
On Regularized Losses for Weakly-supervised CNN Segmentation
Meng Tang, Federico Perazzi, Abdelaziz Djelouah +3
Minimization of regularized losses is a principled approach to weak supervision well-established in deep learning, in general. However, it is largely overlooked in semantic segment…
Normalized Cut Loss for Weakly-supervised CNN Segmentation
Meng Tang, Abdelaziz Djelouah, Federico Perazzi +2
Most recent semantic segmentation methods train deep convolutional neural networks with fully annotated masks requiring pixel-accuracy for good quality training. Common weakly-supe…