• Publications
  • Influence
The Pascal Visual Object Classes (VOC) Challenge
The state-of-the-art in evaluated methods for both classification and detection are reviewed, whether the methods are statistically different, what they are learning from the images, and what the methods find easy or confuse. Expand
Speeded-Up Robust Features (SURF)
A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. Expand
SURF: Speeded Up Robust Features
In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previouslyExpand
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Deep convolutional networks have achieved great success for visual recognition in still images. However, for action recognition in videos, the advantage over traditional methods is not so evident.Expand
The Pascal Visual Object Classes Challenge: A Retrospective
A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges. Expand
A Comparison of Affine Region Detectors
A snapshot of the state of the art in affine covariant region detectors, and compares their performance on a set of test images under varying imaging conditions to establish a reference test set of images and performance software so that future detectors can be evaluated in the same framework. Expand
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
This work presents a new benchmark dataset and evaluation methodology for the area of video object segmentation, named DAVIS (Densely Annotated VIdeo Segmentation), and provides a comprehensive analysis of several state-of-the-art segmentation approaches using three complementary metrics. Expand
Anchored Neighborhood Regression for Fast Example-Based Super-Resolution
  • R. Timofte, V. Smet, L. Gool
  • Computer Science, Mathematics
  • IEEE International Conference on Computer Vision
  • 1 December 2013
This paper proposes fast super-resolution methods while making no compromise on quality, and supports the use of sparse learned dictionaries in combination with neighbor embedding methods, and proposes the anchored neighborhood regression. Expand
The PASCAL visual object classes challenge 2006 (VOC2006) results
This report presents the results of the 2006 PASCAL Visual Object Classes Challenge (VOC2006). Details of the challenge, data, and evaluation are presented. Participants in the challenge submittedExpand
You'll never walk alone: Modeling social behavior for multi-target tracking
A model of dynamic social behavior, inspired by models developed for crowd simulation, is introduced, trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera. Expand