1 to 5 of 5 Results
Dec 21, 2023
Marquer, Esteban; Miguel Couceiro, 2023, "Données de réplication pour : "Solving morphological analogies: from retrieval to generation"", https://doi.org/10.12763/I5ED78, Université de Lorraine, V1, UNF:6:JOLK+VaihCp0kH5dTg5ROA== [fileUNF]
This repository contains the models trained for the article "Solving morphological analogies: from retrieval to generation". The data is split in 3 folders: "models", "results", and "logs". The folders "models" and "result", respectively found as "model.zip" and "results.zip", co... |
Jan 19, 2023
Zins, Matthieu, 2023, "OA-SLAM data/weights", https://doi.org/10.12763/2CZWJP, Université de Lorraine, V1
Test sequences of two indoor scenes used to evaluate semantic visual SLAM (Simultaneous Localization And Mapping). This repository also contains Yolo v5 weights for object detections, either pretrained on COCO dataset or fine-tuned on statues and museum objects. This data can be... |
Jan 19, 2023
Zins, Matthieu, 2023, "3D-Aware Ellipses for Visual Localization", https://doi.org/10.12763/N6LHZF, Université de Lorraine, V1
This repository contains trained weights for object detection and ellipse prediction for visual localization. These weights were trained on the Chess scene of the 7-Scenes dataset and can be loaded in the neural networks available at https://gitlab.inria.fr/tangram/3d-aware-ellip... |
May 9, 2022
Richard, Valentin D., 2022, "Meta-ACG preprocessor", https://doi.org/10.12763/VWKNSA, Université de Lorraine, V2
The meta-ACG preprocessor (MACG) is a python program providing an environment to develop and test abstract categorial grammars (ACG) with feature structures. This program takes an input grammar, written in the meta-ACG language (.macg format), and outputs an ACG grammar, which ca... |
Mar 16, 2022
Marquer, Esteban; Couceiro, Miguel; Safa Alsaidi; Amandine Decker, 2022, "Siganalogies - morphological analogies from Sigmorphon 2016 and 2019", https://doi.org/10.12763/MLCFIE, Université de Lorraine, V1
The siganalogies dataset contains morphological analogies built upon Sigmorphon 2016 and Sigmorphon 2019 in PyTorch. An analogical proportion is defined as a 4-ary relation written A:B::C:D and which reads "A is to B as C is to D". In this dataset, we manipulate morphological ana... |