Search Results for author: Georg Heigold

Found 12 papers, 6 papers with code

Video OWL-ViT: Temporally-consistent open-world localization in video

no code implementations22 Aug 2023 Georg Heigold, Matthias Minderer, Alexey Gritsenko, Alex Bewley, Daniel Keysers, Mario Lučić, Fisher Yu, Thomas Kipf

Our model is end-to-end trainable on video data and enjoys improved temporal consistency compared to tracking-by-detection baselines, while retaining the open-world capabilities of the backbone detector.

Object Localization

Conditional Object-Centric Learning from Video

2 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

6 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 #8 on Action Classification on Moments in Time (Top 5 Accuracy metric, using extra training data)

Action Classification Action Recognition +4

Object-Centric Learning with Slot Attention

8 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 Property Prediction

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

1 code implementation21 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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