986 citations · 2.4k across the 21 of their papers we have counts for
22 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…
Person Recognition in Personal Photo Collections
Seong Joon Oh, Rodrigo Benenson, Mario Fritz +1
People nowadays share large parts of their personal lives through social media. Being able to automatically recognise people in personal photos may greatly enhance user convenience…
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-…
Lucid Data Dreaming for Video Object Segmentation
Anna Khoreva, Rodrigo Benenson, Eddy Ilg +2
Convolutional networks reach top quality in pixel-level video object segmentation but require a large amount of training data (1k~100k) to deliver such results. We propose a new tr…
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…
Exploiting saliency for object segmentation from image level labels
Seong Joon Oh, Rodrigo Benenson, Anna Khoreva +3
There have been remarkable improvements in the semantic labelling task in the recent years. However, the state of the art methods rely on large-scale pixel-level annotations. This…
Learning Video Object Segmentation from Static Images
Anna Khoreva, Federico Perazzi, Rodrigo Benenson +2
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentati…
Faceless Person Recognition; Privacy Implications in Social Media
Seong Joon Oh, Rodrigo Benenson, Mario Fritz +1
As we shift more of our lives into the virtual domain, the volume of data shared on the web keeps increasing and presents a threat to our privacy. This works contributes to the und…
Improved Image Boundaries for Better Video Segmentation
Anna Khoreva, Rodrigo Benenson, Fabio Galasso +2
Graph-based video segmentation methods rely on superpixels as starting point. While most previous work has focused on the construction of the graph edges and weights as well as sol…
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…
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Anna Khoreva, Rodrigo Benenson, Jan Hosang +2
Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annota…
How Far are We from Solving Pedestrian Detection?
Shanshan Zhang, Rodrigo Benenson, Mohamed Omran +2
Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the "perfect single frame detector". We enable our an…
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…
Ten Years of Pedestrian Detection, What Have We Learned?
Rodrigo Benenson, Mohamed Omran, Jan Hosang +1
Paper-by-paper results make it easy to miss the forest for the trees.We analyse the remarkable progress of the last decade by discussing the main ideas explored in the 40+ detector…
How good are detection proposals, really?
Jan Hosang, Rodrigo Benenson, Bernt Schiele
Current top performing Pascal VOC object detectors employ detection proposals to guide the search for objects thereby avoiding exhaustive sliding window search across images. Despi…