8 citations · 8 across the 2 of their papers we have counts for
5 papers
DeepPrivacy2: Towards Realistic Full-Body Anonymization
Håkon Hukkelås, Frank Lindseth
Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures. However, current state-of-the-art limit anonymization to the task of face anonymizatio…
Image Inpainting with Learnable Feature Imputation
Håkon Hukkelås, Frank Lindseth, Rudolf Mester
A regular convolution layer applying a filter in the same way over known and unknown areas causes visual artifacts in the inpainted image. Several studies address this issue with f…
DeepPrivacy: A Generative Adversarial Network for Face Anonymization
Håkon Hukkelås, Rudolf Mester, Frank Lindseth
We propose a novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution. We ensure total anonymization of all faces…
Multimodal 3D Object Detection from Simulated Pretraining
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. In order for…
Autonomous Vehicle Control: End-to-end Learning in Simulated Urban Environments
Hege Haavaldsen, Max Aasboe, Frank Lindseth
In recent years, considerable progress has been made towards a vehicle's ability to operate autonomously. An end-to-end approach attempts to achieve autonomous driving using a sing…