R
Rodrigo Benenson
Researcher at Max Planck Society
Publications - 65
Citations - 22139
Rodrigo Benenson is an academic researcher from Max Planck Society. The author has contributed to research in topics: Object detection & Pedestrian detection. The author has an hindex of 46, co-authored 65 publications receiving 16169 citations. Previous affiliations of Rodrigo Benenson include Google & Katholieke Universiteit Leuven.
Papers
More filters
Proceedings ArticleDOI
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Rehfeld,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele +8 more
TL;DR: This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity.
Posted Content
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Rehfeld,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele +8 more
TL;DR: Cityscapes as discussed by the authors is a large-scale dataset for semantic urban scene understanding, consisting of 5000 images with high quality pixel-level annotations and 200,000 additional images with coarse annotations.
Journal ArticleDOI
What Makes for Effective Detection Proposals
TL;DR: An in-depth analysis of twelve proposal methods along with four baselines regarding proposal repeatability, ground truth annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM, R-CNN, and Fast R- CNN detection performance shows that for object detection improving proposal localisation accuracy is as important as improving recall.
Book ChapterDOI
Ten Years of Pedestrian Detection, What Have We Learned?
TL;DR: This work analyzes the remarkable progress of the last decade by dis- cussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark to find a new decision forest detector.
Proceedings ArticleDOI
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
TL;DR: The authors proposed a weak supervision approach that does not require modification of the segmentation training procedure, and showed that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results.