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From the 1 of 26 linked papers with an AI index.

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20152020
most citedAddressing Failure Prediction by Learning Model Confidence

106 citations · 113 across the 8 of their papers we have counts for

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cs.CV2020

Artificial Dummies for Urban Dataset Augmentation

Antonín Vobecký, David Hurych, Michal Uřičář +2

Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are…

cs.CV2020

OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning

Spyros Gidaris, Andrei Bursuc, Gilles Puy +3

Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a…

cs.CV2020

Photo style transfer with consistency losses

Xu Yao, Gilles Puy, Patrick Pérez

We address the problem of style transfer between two photos and propose a new way to preserve photorealism. Using the single pair of photos available as input, we train a pair of d…

cs.CV2020

StyleRig: Rigging StyleGAN for 3D Control over Portrait Images

Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj +5

StyleGAN generates photorealistic portrait images of faces with eyes, teeth, hair and context (neck, shoulders, background), but lacks a rig-like control over semantic face paramet…

cs.CV2019

xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation

Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette +2

Unsupervised Domain Adaptation (UDA) is crucial to tackle the lack of annotations in a new domain. There are many multi-modal datasets, but most UDA approaches are uni-modal. In th…

cs.CV2019106 cited

Addressing Failure Prediction by Learning Model Confidence

Charles Corbière, Nicolas Thome, Avner Bar-Hen +2

Assessing reliably the confidence of a deep neural network and predicting its failures is of primary importance for the practical deployment of these models. In this paper, we prop…