Search Results for author: Georg Heigold

Found 11 papers, 4 papers with code

Conditional Object-Centric Learning from Video

no code implementations ICLR 2022 Thomas Kipf, Gamaleldin F. Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jonschkowski, Alexey Dosovitskiy, Klaus Greff

Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built.

Instance Segmentation Optical Flow Estimation +2

ViViT: A Video Vision Transformer

4 code implementations ICCV 2021 Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, Cordelia Schmid

We present pure-transformer based models for video classification, drawing upon the recent success of such models in image classification.

Ranked #6 on Action Classification on Moments in Time (Top 5 Accuracy metric, using extra training data)

Action Classification Action Recognition +3

Object-Centric Learning with Slot Attention

4 code implementations NeurIPS 2020 Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf

Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features.

Object Discovery

Cross-lingual Character-Level Neural Morphological Tagging

no code implementations EMNLP 2017 Ryan Cotterell, Georg Heigold

Even for common NLP tasks, sufficient supervision is not available in many languages {--} morphological tagging is no exception.

Language Modelling Morphological Tagging +2

Cross-lingual, Character-Level Neural Morphological Tagging

no code implementations30 Aug 2017 Ryan Cotterell, Georg Heigold

Even for common NLP tasks, sufficient supervision is not available in many languages -- morphological tagging is no exception.

Morphological Tagging Transfer Learning

Neural Morphological Tagging from Characters for Morphologically Rich Languages

no code implementations21 Jun 2016 Georg Heigold, Guenter Neumann, Josef van Genabith

We systematically explore a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain character-based word vectors combined with bidirectional LSTMs to model across-word context in an end-to-end setting.

Morphological Tagging TAG +1

End-to-End Text-Dependent Speaker Verification

3 code implementations27 Sep 2015 Georg Heigold, Ignacio Moreno, Samy Bengio, Noam Shazeer

In this paper we present a data-driven, integrated approach to speaker verification, which maps a test utterance and a few reference utterances directly to a single score for verification and jointly optimizes the system's components using the same evaluation protocol and metric as at test time.

Text-Dependent Speaker Verification

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