• Publications
  • Influence
Speeded-Up Robust Features (SURF)
TLDR
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
A Comparison of Affine Region Detectors
TLDR
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
Unsupervised Visual Domain Adaptation Using Subspace Alignment
TLDR
This paper introduces a new domain adaptation algorithm where the source and target domains are represented by subspaces described by eigenvectors, and seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one. Expand
Pose Guided Person Image Generation
TLDR
The novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose, is proposed. Expand
Memory Aware Synapses: Learning what (not) to forget
TLDR
This paper argues that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively and proposes a novel approach for lifelong learning, coined Memory Aware Synapses (MAS), which computes the importance of the parameters of a neural network in an unsupervised and online manner. Expand
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
TLDR
This paper presents for the first time spatio-temporal interest points that are at the same time scale-invariant (both spatially and temporally) and densely cover the video content and can be computed efficiently. Expand
Dynamic Filter Networks
TLDR
The Dynamic Filter Network is introduced, where filters are generated dynamically conditioned on an input, and it is shown that this architecture is a powerful one, with increased flexibility thanks to its adaptive nature, yet without an excessive increase in the number of model parameters. Expand
Matching Widely Separated Views Based on Affine Invariant Regions
  • T. Tuytelaars, L. Gool
  • Mathematics, Computer Science
  • International Journal of Computer Vision
  • 1 August 2004
TLDR
To increase the robustness of the system, two semi-local constraints on combinations of region correspondences are derived (one geometric, the other photometric) allow to test the consistency of correspondences and hence to reject falsely matched regions. Expand
Object Detection by Contour Segment Networks
TLDR
An extensive experimental evaluation on detecting five diverse object classes over hundreds of images demonstrates that the proposed method works in very cluttered images, allows for scale changes and considerable intra-class shape variation, is robust to interrupted contours, and is computationally efficient. Expand
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