Search Results for author: Radek Mackowiak

Found 5 papers, 2 papers with code

Towards Multimodal Depth Estimation from Light Fields

no code implementations CVPR 2022 Titus Leistner, Radek Mackowiak, Lynton Ardizzone, Ullrich Köthe, Carsten Rother

We argue that this is due current methods only considering a single "true" depth, even when multiple objects at different depths contributed to the color of a single pixel.

Depth Estimation Depth Prediction

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification

3 code implementations NeurIPS 2020 Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe

In this work, firstly, we develop the theory and methodology of IB-INNs, a class of conditional normalizing flows where INNs are trained using the IB objective: Introducing a small amount of {\em controlled} information loss allows for an asymptotically exact formulation of the IB, while keeping the INN's generative capabilities intact.

General Classification Out-of-Distribution Detection

Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift

no code implementations19 Sep 2019 Titus Leistner, Hendrik Schilling, Radek Mackowiak, Stefan Gumhold, Carsten Rother

In order to work with wide-baseline light fields, we introduce the idea of EPI-Shift: To virtually shift the light field stack which enables to retain a small receptive field, independent of the disparity range.

Depth Estimation

CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation

no code implementations23 Oct 2018 Radek Mackowiak, Philip Lenz, Omair Ghori, Ferran Diego, Oliver Lange, Carsten Rother

State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images.

Active Learning Image Segmentation +1

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