Jan Hosang (PhD Student)

Dipl.-Inform. Jan Hendrik Hosang

Address
Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
Location
E1 4 - Room 626
Phone
+49 681 9325 2123
Fax
+49 681 9325 2099
Email
Get email via email

Personal Information

Research Interests

  • Computer Vision
  • Machine Learning

Short-Bio

2005-2011 RWTH Aachen University, Germany
2008–2009 Imperial College London, United Kingdom

For more information, please visit my personal homepage.

 

Publications

2016
How Far are We from Solving Pedestrian Detection?
S. Zhang, R. Benenson, M. Omran, J. Hosang and B. Schiele
29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016), 2016
What Makes for Effective Detection Proposals?
J. Hosang, R. Benenson, P. Dollár and B. Schiele
IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 38, Number 4, 2016
A Convnet for Non-maximum Suppression
J. Hosang, R. Benenson and B. Schiele
Pattern Recognition (GCPR 2016), 2016
Abstract
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 proposal methods have been extensively researched surprisingly little work has aimed to systematically address NMS. The de-facto standard for NMS is based on greedy clustering with a fixed distance threshold, which forces to trade-off recall versus precision. We propose a convnet designed to perform NMS of a given set of detections. We report experiments on a synthetic setup, and results on crowded pedestrian detection scenes. Our approach overcomes the intrinsic limitations of greedy NMS, obtaining better recall and precision.
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
A. Khoreva, R. Benenson, J. Hosang, M. Hein and B. Schiele
Technical Report, 2016
(arXiv: 1603.07485)
Abstract
Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose to recursively train a convnet such that outputs are improved after each iteration. We explore which aspects affect the recursive training, and which is the most suitable box-guided segmentation to use as initialisation. Our results improve significantly over previously reported ones, even when using rectangles as rough initialisation. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.
2015
Taking a Deeper Look at Pedestrians
J. Hosang, M. Omran, R. Benenson and B. Schiele
IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), 2015
GyroPen: Gyroscopes for Pen-Input with Mobile Phones
T. Deselaers, D. Keysers, J. Hosang and H. Rowley
IEEE Transactions on Human-Machine Systems, Volume 45, Number 2, 2015
What Makes for Effective Detection Proposals?
J. Hosang, R. Benenson, P. Dollár and B. Schiele
Technical Report, 2015
(arXiv: 1502.05082)
Abstract
Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines regarding proposal repeatability, ground truth annotation recall on PASCAL and ImageNet, and impact on DPM and R-CNN detection performance. Our analysis shows that for object detection improving proposal localisation accuracy is as important as improving recall. We introduce a novel metric, the average recall (AR), which rewards both high recall and good localisation and correlates surprisingly well with detector performance. Our findings show common strengths and weaknesses of existing methods, and provide insights and metrics for selecting and tuning proposal methods.
2014
Ten Years of Pedestrian Detection, What Have We Learned?
R. Benenson, M. Omran, J. Hosang and B. Schiele
Computer Vision - ECCV 2014 Workshops (ECCV 2014 Workshop CVRSUAD), 2014
How Good are Detection Proposals, really?
J. Hosang, R. Benenson and B. Schiele
Proceedings of the British Machine Vision Conference (BMVC 2014), 2014
Abstract
Current top performing Pascal VOC object detectors employ detection proposals to guide the search for objects thereby avoiding exhaustive sliding window search across images. Despite the popularity of detection proposals, it is unclear which trade‐offs are made when using them during object detection. We provide an in depth analysis of ten object proposal methods along with four baselines regarding ground truth annotation recall (on Pascal VOC 2007 and ImageNet 2013), repeatability, and impact on DPM detector performance. Our findings show common weaknesses of existing methods, and provide insights to choose the most adequate method for different settings.