no code implementations • 15 Jan 2024 • Mamoona Birkhez Shami, Gabriel Kiss, Trond Arve Haakonsen, Frank Lindseth
We present a measurement pipeline suitable for autonomous driving (AD) platforms and provide detailed guidelines for calibrating cameras using NVIDIA DriveWorks.
1 code implementation • 8 Jun 2023 • Håkon Hukkelås, Frank Lindseth
Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization.
1 code implementation • 6 Apr 2023 • Håkon Hukkelås, Frank Lindseth
Our main contribution is TriA-GAN, a keypoint-guided GAN that can synthesize Anyone, Anywhere, in Any given pose.
no code implementations • 20 Mar 2023 • Tejaswini Medi, Jawad Tayyub, Muhammad Sarmad, Frank Lindseth, Margret Keuper
Implicit generative models have been widely employed to model 3D data and have recently proven to be successful in encoding and generating high-quality 3D shapes.
1 code implementation • 28 Nov 2022 • Javier Pérez de Frutos, André Pedersen, Egidijus Pelanis, David Bouget, Shanmugapriya Survarachakan, Thomas Langø, Ole-Jakob Elle, Frank Lindseth
Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph.
1 code implementation • 17 Nov 2022 • Håkon Hukkelås, Frank Lindseth
Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures.
1 code implementation • 6 Jan 2022 • Håkon Hukkelås, Morten Smebye, Rudolf Mester, Frank Lindseth
Recent work on image anonymization has shown that generative adversarial networks (GANs) can generate near-photorealistic faces to anonymize individuals.
1 code implementation • 21 Dec 2021 • Vemund Fredriksen, Svein Ole M. Svele, André Pedersen, Thomas Langø, Gabriel Kiss, Frank Lindseth
Purpose: Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel.
1 code implementation • 2 Nov 2020 • Håkon Hukkelås, Frank Lindseth, Rudolf Mester
We propose (layer-wise) feature imputation of the missing input values to a convolution.
2 code implementations • 10 Sep 2019 • Håkon Hukkelås, Rudolf Mester, Frank Lindseth
Our model is based on a conditional generative adversarial network, generating images considering the original pose and image background.
Ranked #1 on Face Anonymization on 2019_test set (using extra training data)
2 code implementations • 19 May 2019 • Åsmund Brekke, Fredrik Vatsendvik, Frank Lindseth
The need for simulated data in autonomous driving applications has become increasingly important, both for validation of pretrained models and for training new models.
1 code implementation • 16 May 2019 • Hege Haavaldsen, Max Aasboe, Frank Lindseth
The results of this paper indicate that end-to-end systems can operate autonomously in simple urban environments.