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Dataset.py
Apr 8, 2024 - VisRecall++: Analysing and Predicting Recallability of Information Visualisations from Gaze Behaviour (Dataset and Reproduction Data)
Python Source Code - 5.7 KB - MD5: bc554567b355511c4dadc543f9209a16
main.py
Apr 8, 2024 - VisRecall++: Analysing and Predicting Recallability of Information Visualisations from Gaze Behaviour (Dataset and Reproduction Data)
Python Source Code - 4.3 KB - MD5: 6dbc9847ff4b0d1c126a047f2c39d330
Model.py
Apr 8, 2024 - VisRecall++: Analysing and Predicting Recallability of Information Visualisations from Gaze Behaviour (Dataset and Reproduction Data)
Python Source Code - 2.0 KB - MD5: 9ec79413fcd39fd024d78aca990b11a5
dataset_new.py
Jan 26, 2024 - SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)
Python Source Code - 519 B - MD5: 52d002345282b7ccec556fd12877bfb8
dataloader for SalChartQA
env.py
Jan 26, 2024 - SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)
Python Source Code - 170 B - MD5: 6fcef81e6722e6dec003ea1a6152d008
python envorinment $TORCH_HOME and $TRANSFORMERS_CACHE
evaluation.py
Jan 26, 2024 - SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)
Python Source Code - 4.1 KB - MD5: 57190d4825e65b73b0a8255e242caa20
evaluation script to load VisSalFormer weights and make predictions
evaluation.sh
Jan 26, 2024 - SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)
Shell Script - 87 B - MD5: 0e9c6b5e196912cee38ae0adc5d4f3eb
bash script to run evaluation.py
model_swin.py
Jan 26, 2024 - SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)
Python Source Code - 4.5 KB - MD5: 5c733608c453403dae142c965bb9f14f
definition of the VisSalFormer model
tokenizer_bert.py
Jan 26, 2024 - SalChartQA: Question-driven Saliency on Information Visualisations (Dataset and Reproduction Data)
Python Source Code - 430 B - MD5: 6b88c19cf6ba462971b9a271141c2f54
tokenizer of Bert
stats-utils.R
Sep 29, 2023 - Supplemental Materials for: "De-emphasise, Aggregate, and Hide: A Study on Interactive Visual Transformations for Group Structures in Network Visualisations"
R Syntax - 7.1 KB - MD5: 79653005b4ce8d386b6b0a5eb6122b91
The source code to produce the bootstrap confidence intervals.
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