Search Results for author: Timo Milbich

Found 13 papers, 10 papers with code

Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning

2 code implementations NeurIPS 2021 Timo Milbich, Karsten Roth, Samarth Sinha, Ludwig Schmidt, Marzyeh Ghassemi, Björn Ommer

Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML.

Metric Learning

iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis

2 code implementations ICCV 2021 Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer

There will be distinctive movement, despite evident variations caused by the stochastic nature of our world.

Frame

Understanding Object Dynamics for Interactive Image-to-Video Synthesis

1 code implementation CVPR 2021 Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer

Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time.

Video Prediction

Stochastic Image-to-Video Synthesis using cINNs

1 code implementation CVPR 2021 Michael Dorkenwald, Timo Milbich, Andreas Blattmann, Robin Rombach, Konstantinos G. Derpanis, Björn Ommer

Video understanding calls for a model to learn the characteristic interplay between static scene content and its dynamics: Given an image, the model must be able to predict a future progression of the portrayed scene and, conversely, a video should be explained in terms of its static image content and all the remaining characteristics not present in the initial frame.

Frame Video Understanding

Behavior-Driven Synthesis of Human Dynamics

1 code implementation CVPR 2021 Andreas Blattmann, Timo Milbich, Michael Dorkenwald, Björn Ommer

Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence.

Human Dynamics

S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning

1 code implementation17 Sep 2020 Karsten Roth, Timo Milbich, Björn Ommer, Joseph Paul Cohen, Marzyeh Ghassemi

Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives.

Ranked #6 on Metric Learning on CARS196 (using extra training data)

Knowledge Distillation Metric Learning

Sharing Matters for Generalization in Deep Metric Learning

no code implementations12 Apr 2020 Timo Milbich, Karsten Roth, Biagio Brattoli, Björn Ommer

The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes.

Metric Learning

PADS: Policy-Adapted Sampling for Visual Similarity Learning

1 code implementation CVPR 2020 Karsten Roth, Timo Milbich, Björn Ommer

Learning visual similarity requires to learn relations, typically between triplets of images.

Ranked #13 on Metric Learning on CUB-200-2011 (using extra training data)

Metric Learning

Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

8 code implementations ICML 2020 Karsten Roth, Timo Milbich, Samarth Sinha, Prateek Gupta, Björn Ommer, Joseph Paul Cohen

Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year.

Metric Learning

Unsupervised Representation Learning by Discovering Reliable Image Relations

no code implementations18 Nov 2019 Timo Milbich, Omair Ghori, Ferran Diego, Björn Ommer

To nevertheless find those relations which can be reliably utilized for learning, we follow a divide-and-conquer strategy: We find reliable similarities by extracting compact groups of images and reliable dissimilarities by partitioning these groups into subsets, converting the complicated overall problem into few reliable local subproblems.

Representation Learning Transfer Learning

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