986 citations · 2.4k across the 21 of their papers we have counts for
7 papers · 1 filter
Weakly Supervised Object Boundaries
Anna Khoreva, Rodrigo Benenson, Mohamed Omran +2
State-of-the-art learning based boundary detection methods require extensive training data. Since labelling object boundaries is one of the most expensive types of annotations, the…
A convnet for non-maximum suppression
Jan Hosang, Rodrigo Benenson, Bernt Schiele
Non-maximum suppression (NMS) is used in virtually all state-of-the-art object detection pipelines. While essential object detection ingredients such as features, classifiers, and…
Person Recognition in Personal Photo Collections
Seong Joon Oh, Rodrigo Benenson, Mario Fritz +1
Recognising persons in everyday photos presents major challenges (occluded faces, different clothing, locations, etc.) for machine vision. We propose a convnet based person recogni…
What is Holding Back Convnets for Detection?
Bojan Pepik, Rodrigo Benenson, Tobias Ritschel +1
Convolutional neural networks have recently shown excellent results in general object detection and many other tasks. Albeit very effective, they involve many user-defined design c…
What makes for effective detection proposals?
Jan Hosang, Rodrigo Benenson, Piotr Dollár +1
Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the pop…
Taking a Deeper Look at Pedestrians
Jan Hosang, Mohamed Omran, Rodrigo Benenson +1
In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection. Despite their recent diverse successes, convnets historically under…
Filtered Channel Features for Pedestrian Detection
Shanshan Zhang, Rodrigo Benenson, Bernt Schiele
This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination…