11 to 20 of 23 Results
Sep 7, 2020 - IWR Visual Learning Lab
Brachmann, Eric, 2020, "Expert Sample Consensus (ESAC) for Visual Re-Localization [Data]", https://doi.org/10.11588/data/GSJE9D, heiDATA, V1
Supplementary training data for visual camera re-localization, particularly pre-computed scene coordinates to the MSR 7Scenes dataset and the Standford 12Scenes dataset. We also provide pre-trained models of our method for the 7Scenes, 12Scenes, Dubrovnik and Aachen (day) dataset... |
Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)
Rehbein, Ines; Ruppenhofer, Josef, 2020, "German causal language annotations and lexicon (verbs, nouns, prepositions) (DE)", https://doi.org/10.11588/data/ZHI94V, heiDATA, V1
Annotations of causal verbs, nouns and prepositions in context and lexicon file for causal verbs, nouns and prepositions. |
Jan 20, 2021 - Empirical Linguistics and Computational Language Modeling (LiMo)
van den Berg, Esther; Korfhage, Katharina; Ruppenhofer, Josef; Wiegand, Michael; Markert, Katja, 2020, "German Twitter Titling Corpus", https://doi.org/10.11588/data/AOSUY6, heiDATA, V2, UNF:6:14BxjwJS7Q3mfI6ei7iBBw== [fileUNF]
The German Titling Twitter Corpus consists of 1904 stance-annotated tweets collected in June/July 2018 mentioning 24 German politicians with a doctoral degree. The Addendum contains an additional 296 stance-annotated tweets from each month of 2018 mentioning 10 politicians with a... |
Open Research Data from the Heidelberg Centre for Transcultural Studies (HCTS). |
Aug 27, 2020
This Dataverse contains visual learning related research data of the Interdisciplinary Center for Scientific Computing at Heidelberg University. Website Visual Learning Lab Heidelberg |
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. |
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. |
Sep 7, 2020 - IWR Visual Learning Lab
Brachmann, Eric, 2020, "Neural-Guided RANSAC for Estimating Epipolar Geometry [Data]", https://doi.org/10.11588/data/PCGYET, heiDATA, V1
Pre-computed sparse feature correspondences for pairs of images (outdoor and indoor) to reproduce the experiments described in our paper, particularly to train and evaluate NG-RANSAC. For more information, also see the code documentation: https://github.com/vislearn/ngransac |
Schepp, Moritz, 2020, "OwnReality API-only web application", https://doi.org/10.11588/data/KZHLS8, heiDATA, V1
This dataset contains the data platform for the research project "OwnReality. To Each His Own Reality". During the course of the project, data was gathered and entered into a database. In general, this platform allows the integration of that data into web based systems such as co... |
