Search Results for author: Judith Bütepage

Found 7 papers, 1 papers with code

Seeing the whole picture instead of a single point: Self-supervised likelihood learning for deep generative models

no code implementations pproximateinference AABI Symposium 2019 Petra Poklukar, Judith Bütepage, Danica Kragic

Recent findings show that deep generative models can judge out-of-distribution samples as more likely than those drawn from the same distribution as the training data.

A Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling

1 code implementation24 Sep 2018 Judith Bütepage, Hedvig Kjellström, Danica Kragic

Therefore, video-based human activity modeling is concerned with a number of tasks such as inferring current and future semantic labels, predicting future continuous observations as well as imagining possible future label and feature sequences.

Action Classification General Classification +2

Detect, anticipate and generate: Semi-supervised recurrent latent variable models for human activity modeling

no code implementations19 Sep 2018 Judith Bütepage, Danica Kragic

In this work we introduce semi-supervised variational recurrent neural networks which are able to a) model temporal distributions over latent factors and the observable feature space, b) incorporate discrete labels such as activity type when available, and c) generate possible future action sequences on both feature and label level.

Affordance Detection

Deep representation learning for human motion prediction and classification

no code implementations CVPR 2017 Judith Bütepage, Michael Black, Danica Kragic, Hedvig Kjellström

To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units.

Action Classification Classification +4

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