no code implementations • 24 Aug 2023 • Jan Warchocki, Teodor Oprescu, Yunhan Wang, Alexandru Damacus, Paul Misterka, Robert-Jan Bruintjes, Attila Lengyel, Ombretta Strafforello, Jan van Gemert
This work explores and measures how current deep temporal action localization models perform in settings constrained by the amount of data or computational power.
no code implementations • 22 Aug 2023 • Tom Edixhoven, Attila Lengyel, Jan van Gemert
In this paper we show how Group Equivariant Convolutional Neural Networks use subsampling to learn to break equivariance to their symmetries.
no code implementations • 31 May 2023 • Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista Rios, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen, Jan van Gemert
The third edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" workshop featured four data-impaired challenges, focusing on addressing the limitations of data availability in training deep learning models for computer vision tasks.
1 code implementation • 25 Nov 2022 • Liang Zeng, Attila Lengyel, Nergis Tömen, Jan van Gemert
For unsupervised semantic segmentation of urban scenes, our method surpasses the previous state-of-the-art baseline by +7. 14% in mIoU on Cityscapes and +6. 65% on KITTI.
no code implementations • 21 Jan 2022 • Attila Lengyel, Robert-Jan Bruintjes, Marcos Baptista Rios, Osman Semih Kayhan, Davide Zambrano, Nergis Tomen, Jan van Gemert
The second edition of the "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges featured five data-impaired challenges, where models are trained from scratch on a reduced number of training samples for various key computer vision tasks.
no code implementations • 23 Oct 2021 • Tuhin Das, Robert-Jan Bruintjes, Attila Lengyel, Jan van Gemert, Sara Beery
While domain adaptation is generally applied on completely synthetic source domains and real target domains, we explore how domain adaptation can be applied when only a single rare class is augmented with simulated samples.
1 code implementation • ICCV 2021 • Attila Lengyel, Sourav Garg, Michael Milford, Jan C. van Gemert
The traditional domain adaptation setting is to train on one domain and adapt to the target domain by exploiting unlabeled data samples from the test set.
Ranked #2 on Image Retrieval on 24/7 Tokyo
1 code implementation • 9 Jun 2021 • Attila Lengyel, Jan C. van Gemert
Group Equivariant Convolutions (GConvs) enable convolutional neural networks to be equivariant to various transformation groups, but at an additional parameter and compute cost.
1 code implementation • 5 Mar 2021 • Robert-Jan Bruintjes, Attila Lengyel, Marcos Baptista Rios, Osman Semih Kayhan, Jan van Gemert
We present the first edition of "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges.
no code implementations • 4 Aug 2020 • David Cian, Jan van Gemert, Attila Lengyel
In this paper, we run two methods of explanation, namely LIME and Grad-CAM, on a convolutional neural network trained to label images with the LEGO bricks that are visible in them.