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Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
David Bruggemann,
Christos Sakaridis,
Prune Truong,
Luc Van Gool
WACV 2023
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We propose Refign, a generic extension to self-training-based domain adaptation methods for semantic segmentation.
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Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences
Prune Truong,
Martin Danelljan,
Fisher Yu,
Luc Van Gool
CVPR 2022
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We propose a weakly-supervised training strategy for learning semantic correspondences. We introduce a
weakly-supervised training objective applied to probabilistic mapping as well as an approach to model
and identify occluded and unmatchable regions.
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Warp Consistency for Unsupervised Learning of Dense Correspondences
Prune Truong,
Martin Danelljan,
Fisher Yu,
Luc Van Gool
ICCV 2021 - Oral (top 3.0%)
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We propose an unsupervised training objective for learning to regress the dense correspondences relating a pair
of images. Our loss leverages real image pairs without invoking the photometric consistency assumption.
Unlike previous approaches, it is capable of handling large appearance and view-point changes.
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GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
Prune Truong,
Martin Danelljan,
Luc Van Gool,
Radu Timofte
NeurIPS 2020
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We propose GOCOr, a fully differentiable dense matching module, acting as a direct replacement to the
feature correlation layer. The correspondence volume generated by our module is the result of an
internal optimization procedure that explicitly accounts for - and suppressed - similar regions in the scene.
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GLU-Net: Global-Local Universal Network for dense flow and correspondences
Prune Truong,
Martin Danelljan,
Luc Van Gool,
Radu Timofte
CVPR 2020 - Oral (top 5.7%)
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We propose GLU-Net, a unified architecture to estimate dense correspondences between any image pair, i.e. different
views of the same scene, consecutive frames of a video or even different instances of the same object category.
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GLAMpoints: Greedily Learned Accurate Match points
Prune Truong,
Stefanos Apostolopoulos,
Agata Mosinska,
Samuel Stucky,
Carlos Ciller,
Sandro De Zanet
ICCV 2019
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We propose a training strategy for keypoint detection, applicable to low-quality and textureless images,
frequent in the medical domain. We learn keypoint detection by training directly for the final matching accuracy
instead of indirect metrics such as repeatability.
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Invited Talks
- 2023: Dense Matching and Its Applications, Invited talk for the Swedish WASP program, Zurich.
- 2022: Dense Matching, Invited talk at Google, Semantic Perception, Zurich.
- 2022: Dense Matching, Invited talk at Microsoft, Mixed Reality and AI Lab, Zurich.
- 2021: PDC-Net and matching challenge, Image Matching Workshop: Local Features & Beyond, CVPR 2021, Virtual.
- 2021: PDC-Net (CVPR 2021), Reading group of Dr. Krystian Mikolajczyk at Matchlab, Virtual.
- 2021: GOCor (NeurIPS 2020), Computer Vision Talks, Virtual.
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