Search Results for author: Christian Rupprecht

Found 40 papers, 15 papers with code

Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion

no code implementations16 May 2022 Subhabrata Choudhury, Laurynas Karazija, Iro Laina, Andrea Vedaldi, Christian Rupprecht

In the unsupervised video segmentation mode, the network is trained on a collection of unlabelled videos, using the learning process itself as an algorithm to segment these videos.

Optical Flow Estimation Semantic Segmentation +3

Thermographic detection of internal defects using 2D photothermal super resolution reconstruction with sequential laser heating

no code implementations1 Mar 2022 Julien Lecompagnon, Samim Ahmadi, Philipp Hirsch, Christian Rupprecht, Mathias Ziegler

Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures.


De-rendering 3D Objects in the Wild

no code implementations CVPR 2022 Felix Wimbauer, Shangzhe Wu, Christian Rupprecht

With increasing focus on augmented and virtual reality applications (XR) comes the demand for algorithms that can lift objects from images and videos into representations that are suitable for a wide variety of related 3D tasks.

ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

1 code implementation19 Nov 2021 Laurynas Karazija, Iro Laina, Christian Rupprecht

We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data.

Semantic Segmentation Unsupervised Object Segmentation

Unsupervised Part Discovery from Contrastive Reconstruction

1 code implementation NeurIPS 2021 Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi

First, we construct a proxy task through a set of objectives that encourages the model to learn a meaningful decomposition of the image into its parts.

Representation Learning

The Curious Layperson: Fine-Grained Image Recognition without Expert Labels

1 code implementation5 Nov 2021 Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi

We then train a fine-grained textual similarity model that matches image descriptions with documents on a sentence-level basis.

Cross-Modal Retrieval Fine-Grained Image Recognition

Learning Context-Adapted Video-Text Retrieval by Attending to User Comments

no code implementations29 Sep 2021 Laura Hanu, Yuki M Asano, James Thewlis, Christian Rupprecht

Learning strong representations for multi-modal retrieval is an important problem for many applications, such as recommendation and search.

Video-Text Retrieval

PASS: An ImageNet replacement for self-supervised pretraining without humans

1 code implementation NeurIPS Workshop ImageNet_PPF 2021 Yuki M. Asano, Christian Rupprecht, Andrew Zisserman, Andrea Vedaldi

On the other hand, state-of-the-art pretraining is nowadays obtained with unsupervised methods, meaning that labelled datasets such as ImageNet may not be necessary, or perhaps not even optimal, for model pretraining.

Ethics Pose Estimation +1

DOVE: Learning Deformable 3D Objects by Watching Videos

no code implementations22 Jul 2021 Shangzhe Wu, Tomas Jakab, Christian Rupprecht, Andrea Vedaldi

In this paper, we present DOVE, a method that learns textured 3D models of deformable object categories from monocular videos available online, without keypoint, viewpoint or template shape supervision.

Labelling unlabelled videos from scratch with multi-modal self-supervision

1 code implementation NeurIPS 2020 Yuki M. Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi

A large part of the current success of deep learning lies in the effectiveness of data -- more precisely: labelled data.

Semantic Image Manipulation Using Scene Graphs

1 code implementation CVPR 2020 Helisa Dhamo, Azade Farshad, Iro Laina, Nassir Navab, Gregory D. Hager, Federico Tombari, Christian Rupprecht

In our work, we address the novel problem of image manipulation from scene graphs, in which a user can edit images by merely applying changes in the nodes or edges of a semantic graph that is generated from the image.

Image Inpainting Image Manipulation +1

Improving Feature Attribution through Input-specific Network Pruning

no code implementations25 Nov 2019 Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab

Attributing the output of a neural network to the contribution of given input elements is a way of shedding light on the black-box nature of neural networks.

Network Pruning

Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

1 code implementation CVPR 2020 Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi

We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision.

Self-labelling via simultaneous clustering and representation learning

4 code implementations ICLR 2020 Yuki Markus Asano, Christian Rupprecht, Andrea Vedaldi

Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks.

Image Clustering Representation Learning +2

Traffic4cast-Traffic Map Movie Forecasting -- Team MIE-Lab

1 code implementation27 Oct 2019 Henry Martin, Ye Hong, Dominik Bucher, Christian Rupprecht, René Buffat

The goal of the IARAI competition traffic4cast was to predict the city-wide traffic status within a 15-minute time window, based on information from the previous hour.

Photo-Geometric Autoencoding to Learn 3D Objects from Unlabelled Images

no code implementations4 Jun 2019 Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi

Specifically, given a single image of the object seen from an arbitrary viewpoint, our model predicts a symmetric canonical view, the corresponding 3D shape and a viewpoint transformation, and trains with the goal of reconstructing the input view, resembling an auto-encoder.

Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents

no code implementations ICLR 2020 Christian Rupprecht, Cyril Ibrahim, Christopher J. Pal

Further, critical states in which a very high or a very low reward can be achieved are often interesting to understand the situational awareness of the system as they can correspond to risky states.

Autonomous Driving Decision Making +1

Dealing with Ambiguity in Robotic Grasping via Multiple Predictions

no code implementations2 Nov 2018 Ghazal Ghazaei, Iro Laina, Christian Rupprecht, Federico Tombari, Nassir Navab, Kianoush Nazarpour

Further, we reformulate the problem of robotic grasping by replacing conventional grasp rectangles with grasp belief maps, which hold more precise location information than a rectangle and account for the uncertainty inherent to the task.

Robotic Grasping

Understanding Regularization to Visualize Convolutional Neural Networks

no code implementations20 Apr 2018 Maximilian Baust, Florian Ludwig, Christian Rupprecht, Matthias Kohl, Stefan Braunewell

Variational methods for revealing visual concepts learned by convolutional neural networks have gained significant attention during the last years.

Guide Me: Interacting with Deep Networks

no code implementations CVPR 2018 Christian Rupprecht, Iro Laina, Nassir Navab, Gregory D. Hager, Federico Tombari

Interaction and collaboration between humans and intelligent machines has become increasingly important as machine learning methods move into real-world applications that involve end users.

Image Captioning Image Generation

Learning to Imagine Manipulation Goals for Robot Task Planning

no code implementations8 Nov 2017 Chris Paxton, Kapil Katyal, Christian Rupprecht, Raman Arora, Gregory D. Hager

Ideally, we would combine the ability of machine learning to leverage big data for learning the semantics of a task, while using techniques from task planning to reliably generalize to new environment.

Robot Task Planning

Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies

no code implementations ICLR 2018 Robert DiPietro, Christian Rupprecht, Nassir Navab, Gregory D. Hager

Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to capture long-term dependencies remains difficult.

Activity Recognition Machine Translation +1

Deep Active Contours

no code implementations18 Jul 2016 Christian Rupprecht, Elizabeth Huaroc, Maximilian Baust, Nassir Navab

We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework.

Interactive Segmentation

A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks

no code implementations24 Jun 2016 Felix Grün, Christian Rupprecht, Nassir Navab, Federico Tombari

Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance.

Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests

no code implementations23 Oct 2015 Kanishka Sharma, Loic Peter, Christian Rupprecht, Anna Caroli, Lichao Wang, Andrea Remuzzi, Maximilian Baust, Nassir Navab

This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data.

Computed Tomography (CT)

Robust Optimization for Deep Regression

1 code implementation ICCV 2015 Vasileios Belagiannis, Christian Rupprecht, Gustavo Carneiro, Nassir Navab

Convolutional Neural Networks (ConvNets) have successfully contributed to improve the accuracy of regression-based methods for computer vision tasks such as human pose estimation, landmark localization, and object detection.

Age Estimation object-detection +2

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