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Xi Shen
Researcher at École des ponts ParisTech
Publications - 18
Citations - 419
Xi Shen is an academic researcher from École des ponts ParisTech. The author has contributed to research in topics: Deep learning & Watermark. The author has an hindex of 8, co-authored 17 publications receiving 242 citations. Previous affiliations of Xi Shen include École Normale Supérieure.
Papers
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Proceedings ArticleDOI
Discovering Visual Patterns in Art Collections With Spatially-Consistent Feature Learning
TL;DR: The key technical insight is to adapt a standard deep feature to this task by fine-tuning it on the specific art collection using self-supervised learning, and spatial consistency between neighbouring feature matches is used as supervisory fine- Tuning signal.
Posted Content
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Shell Xu Hu,Pablo Garcia Moreno,Yang Xiao,Xi Shen,Guillaume Obozinski,Neil D. Lawrence,Andreas Damianou +6 more
TL;DR: A novel amortized variational inference that couples all the variational posteriors into a meta-model, which consists of a synthetic gradient network and an initialization network that allows for backpropagating information from unlabeled data, thereby enabling transduction.
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Marginalized Average Attentional Network for Weakly-Supervised Learning
TL;DR: Theoretically, it is proved that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others, and therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos.
Book ChapterDOI
RANSAC-Flow: Generic Two-Stage Image Alignment
TL;DR: In this paper, a parametric and non-parametric alignment method is proposed for dense alignment between two images, whether they be two frames of a video, two widely different views of a scene, two paintings depicting similar content, etc.
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RANSAC-Flow: generic two-stage image alignment
TL;DR: This paper considers the generic problem of dense alignment between two images and proposes a two-stage process: first, a feature-based parametric coarse alignment using one or more homographies, followed by non-parametric fine pixel-wise alignment.