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20182022
most citedSegmentation and Recovery of Superquadric Models using Convolutional Neural Networks

5 citations · 5 across the 2 of their papers we have counts for

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cs.CV2022

Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition

Klemen Grm, Berk Kemal Özata, Vitomir Štruc +1

In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g.,…

cs.CV2021

MFR 2021: Masked Face Recognition Competition

Fadi Boutros, Naser Damer, Jan Niklas Kolf +32

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attra…

cs.CV20205 cited

Segmentation and Recovery of Superquadric Models using Convolutional Neural Networks

Jaka Šircelj, Tim Oblak, Klemen Grm +5

In this paper we address the problem of representing 3D visual data with parameterized volumetric shape primitives. Specifically, we present a (two-stage) approach built around con…

cs.CV2019

Recovery of Superquadrics from Range Images using Deep Learning: A Preliminary Study

Tim Oblak, Klemen Grm, Aleš Jaklič +3

It has been a longstanding goal in computer vision to describe the 3D physical space in terms of parameterized volumetric models that would allow autonomous machines to understand…

cs.CV2018

Face Hallucination Revisited: An Exploratory Study on Dataset Bias

Klemen Grm, Martin Pernuš, Leo Cluzel +3

Contemporary face hallucination (FH) models exhibit considerable ability to reconstruct high-resolution (HR) details from low-resolution (LR) face images. This ability is commonly…

cs.CV2018

Face hallucination using cascaded super-resolution and identity priors

Klemen Grm, Simon Dobrišek, Walter J. Scheirer +1

In this paper we address the problem of hallucinating high-resolution facial images from unaligned low-resolution inputs at high magnification factors. We approach the problem with…