67 citations · 112 across the 3 of their papers we have counts for
5 papers
From colouring-in to pointillism: revisiting semantic segmentation supervision
Rodrigo Benenson, Vittorio Ferrari
The prevailing paradigm for producing semantic segmentation training data relies on densely labelling each pixel of each image in the training set, akin to colouring-in books. This…
Large-scale interactive object segmentation with human annotators
Rodrigo Benenson, Stefan Popov, Vittorio Ferrari
Manually annotating object segmentation masks is very time consuming. Interactive object segmentation methods offer a more efficient alternative where a human annotator and a machi…
Learning non-maximum suppression
Jan Hosang, Rodrigo Benenson, Bernt Schiele
Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, features, and the classifier becoming one neural network improved results two-…
CityPersons: A Diverse Dataset for Pedestrian Detection
Shanshan Zhang, Rodrigo Benenson, Bernt Schiele
Convnets have enabled significant progress in pedestrian detection recently, but there are still open questions regarding suitable architectures and training data. We revisit CNN d…
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos +6
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, esp…