L
Luc Van Gool
Researcher at Katholieke Universiteit Leuven
Publications - 1458
Citations - 137230
Luc Van Gool is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 133, co-authored 1307 publications receiving 107743 citations. Previous affiliations of Luc Van Gool include Microsoft & ETH Zurich.
Papers
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Book ChapterDOI
Face Detection without Bells and Whistles
TL;DR: It is shown that a properly trained vanilla DPM reaches top performance, improving over commercial and research systems, and a detector based on rigid templates - similar in structure to the Viola&Jones detector - can reach similar top performance on this task.
Journal ArticleDOI
Matching Widely Separated Views Based on Affine Invariant Regions
Tinne Tuytelaars,Luc Van Gool +1 more
TL;DR: 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.
Journal ArticleDOI
Temporal Segment Networks for Action Recognition in Videos
TL;DR: Temporal Segment Networks (TSN) as discussed by the authors is proposed to model long-range temporal structure with a new segment-based sampling and aggregation scheme, which enables the TSN framework to efficiently learn action models by using the whole video.
Proceedings Article
Pose Guided Person Image Generation
TL;DR: Zhang et al. as discussed by the authors proposed a 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.
Proceedings ArticleDOI
Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions
Tinne Tuytelaars,Luc Van Gool +1 more
TL;DR: This work presents an alternative method for extracting invariant regions that does not depend on the presence of edges or corners in the image but is purely intensity-based, and demonstrates the use of such regions for another application, which is wide baseline stereo matching.