Results 21 to 30 of about 1,907,225 (252)
Ground-truth labeling and inferred detections for medium-complexity images.
, 2021 Comparison between the ground truth and the inferred detections in medium-complexity images. On the left is an image labeled by a medical expert. The results of the trained model are provided on the right. (TIF)
Leonardo Vanneschi (11767436) +6 more
core +1 more source
, 2023 use of NumberingZone in B series adding D series (in test and in train) adding character table updating citation model minor update in the transcription guidelinesIf you use this dataset, please cite it using the metadata from this ...
Chagué, Alix, Pérez, Gilles
core +1 more source
Ground Truth for Layout Analysis Performance Evaluation
, 2006 Over the past two decades a significant number of layout analysis (page segmentation and region classification) approaches have been proposed in the literature.
Bridson, David +2 more
core +2 more sources
Applied Computing and Geosciences, 2023 Lake Chad is facing critical situations since the 1960s due to the effects of climate change and anthropogenic activities. The statistical analyses of remote sensing climate variables (i.e., evapotranspiration, specific humidity, soil temperature, air ...
Kim-Ndor Djimadoumngar
doaj +1 more source
A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning
IEEE Access, 2023 Multi-view partial multi-label learning (MVPML) is a fundenmental problem where each sample is linked to multiple kinds of features and candidate labels, including ground-truth and noise labels.
Jiazheng Yuan +3 more
doaj +1 more source
A Newly Developed Ground Truth Dataset for Visual Saliency in Videos
IEEE Access, 2018 Visual saliency models aim to detect important and eye catching portions in a scene by exploiting human visual system characteristics. The effectiveness of visual saliency models is evaluated by comparing saliency maps with a ground truth data set.
Muhammad Zeeshan +5 more
doaj +1 more source
Evaluating explainability for graph neural networks
Scientific Data, 2023 As explanations are increasingly used to understand the behavior of graph neural networks (GNNs), evaluating the quality and reliability of GNN explanations is crucial.
Chirag Agarwal +3 more
doaj +1 more source
Ground-truth (GT) and predictions in Split 3 of FluentSigners-50 for SLR.
, 2022 Ground-truth (GT) and predictions in Split 3 of FluentSigners-50 for SLR.
Vadim Kimmelman (8921639) +5 more
core +1 more source
HMPLMD: Handwritten Malayalam palm leaf manuscript dataset
Data in Brief, 2023 The realization of high recognition rates of degraded documents such as palm leaf manuscripts primarily relies on document enhancement. Advancement of deep learning models in the process of document enhancement plays a major role among non-deep learning ...
B.J. Bipin Nair, N. Shobha Rani
doaj +1 more source
, 2021 Organizational decision-makers need to evaluate AI tools in light of increasing claims that such tools outperform human experts. Yet, measuring the quality of knowledge work is challenging, raising the question of how to evaluate AI performance in such ...
Lifshitz-Assa, Hila +5 more
core +1 more source