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1 to 6 of 6 Results

Differentiable RANSAC (DSAC) for Visual Re-Localization [Data] Sep 7, 2020 - IWR Visual Learning Lab Brachmann, Eric, 2020, "Differentiable RANSAC (DSAC) for Visual Re-Localization [Data]", https://doi.org/10.11588/data/3JVZSH, heiDATA, V1 Pre-trained models of our camera re-localization method for the MSR 7Scenes dataset. For more information, also see the code documentation: https://github.com/cvlab-dresden/DSAC
6D Object Pose Estimation using 3D Object Coordinates [Data] Sep 7, 2020 - IWR Visual Learning Lab Brachmann, Eric, 2020, "6D Object Pose Estimation using 3D Object Coordinates [Data]", https://doi.org/10.11588/data/V4MUMX, heiDATA, V1 Supplementary training data and binaries for 6D object pose estimation, particularly a dataset of 20 objects under various lighting conditions with RGB-D images, ground truth poses and segmentation as well as 3D models. Additionally, a collection of RGB-D images showing office ba...
Pre-trained POS tagging models for German social media Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1 Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of th...
MACE-AL-TREE Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef, 2020, "MACE-AL-TREE", https://doi.org/10.11588/data/THPEBR, heiDATA, V1 An method for detecting noise in automatically annotated dependency parse trees, combining MACE (Hovy et al. 2013) with Active Learning.
MACE-AL Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Ruppenhofer, Josef; Steen, Julius, 2020, "MACE-AL", https://doi.org/10.11588/data/C2OQN4, heiDATA, V1 A method for detecting noise in automatically annotated sequence-labelled data, combining MACE (Hovy et al. 2013) with Active Learning.
3D Micro-Mapping of Subsidence Stations [Source Code and Data] Jan 15, 2020 - 3D Spatial Data Processing Herfort, Benjamin; Anders, Katharina; Marx, Sabrina; Eberlein, Stefan; Höfle, Bernhard, 2020, "3D Micro-Mapping of Subsidence Stations [Source Code and Data]", https://doi.org/10.11588/data/OU8YA1, heiDATA, V1 This dataset comprises the source code to reproduce the 3D micro-mapping tool for plane adjustment at subsidence stations. In this project, users adjust a plane (height and orientation) at the positions of fixed poles, so-called subsidence stations, to provide information on the...

Differentiable RANSAC (DSAC) for Visual Re-Localization [Data]

Sep 7, 2020 - IWR Visual Learning Lab

Brachmann, Eric, 2020, "Differentiable RANSAC (DSAC) for Visual Re-Localization [Data]", https://doi.org/10.11588/data/3JVZSH, heiDATA, V1

Pre-trained models of our camera re-localization method for the MSR 7Scenes dataset. For more information, also see the code documentation: https://github.com/cvlab-dresden/DSAC

6D Object Pose Estimation using 3D Object Coordinates [Data]

Sep 7, 2020 - IWR Visual Learning Lab

Brachmann, Eric, 2020, "6D Object Pose Estimation using 3D Object Coordinates [Data]", https://doi.org/10.11588/data/V4MUMX, heiDATA, V1

Supplementary training data and binaries for 6D object pose estimation, particularly a dataset of 20 objects under various lighting conditions with RGB-D images, ground truth poses and segmentation as well as 3D models. Additionally, a collection of RGB-D images showing office ba...

Pre-trained POS tagging models for German social media

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Ruppenhofer, Josef; Zimmermann, Victor, 2020, "Pre-trained POS tagging models for German social media", https://doi.org/10.11588/data/W3JBV4, heiDATA, V1

Pre-trained POS tagging models for the HunPos tagger (Halácsy et al. 2007) the biLSTM-char-CRF tagger (Reimers & Gurevych 2017) Online-Flors (Yin et al. 2015). References: Halácsy, P., Kornai, A., and Oravecz, C. (2007). HunPos: An open source trigram tagger. In Proceedings of th...

MACE-AL-TREE

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Ruppenhofer, Josef, 2020, "MACE-AL-TREE", https://doi.org/10.11588/data/THPEBR, heiDATA, V1

An method for detecting noise in automatically annotated dependency parse trees, combining MACE (Hovy et al. 2013) with Active Learning.

MACE-AL

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Ruppenhofer, Josef; Steen, Julius, 2020, "MACE-AL", https://doi.org/10.11588/data/C2OQN4, heiDATA, V1

A method for detecting noise in automatically annotated sequence-labelled data, combining MACE (Hovy et al. 2013) with Active Learning.

3D Micro-Mapping of Subsidence Stations [Source Code and Data]

Jan 15, 2020 - 3D Spatial Data Processing

Herfort, Benjamin; Anders, Katharina; Marx, Sabrina; Eberlein, Stefan; Höfle, Bernhard, 2020, "3D Micro-Mapping of Subsidence Stations [Source Code and Data]", https://doi.org/10.11588/data/OU8YA1, heiDATA, V1

This dataset comprises the source code to reproduce the 3D micro-mapping tool for plane adjustment at subsidence stations. In this project, users adjust a plane (height and orientation) at the positions of fixed poles, so-called subsidence stations, to provide information on the...

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