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9,741 to 9,750 of 9,985 Results

wsj168.png Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" PNG Image - 1.2 MB - MD5: cc9cc9c75b49a5423da365b19d4d9979
wsj593.png Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" PNG Image - 373.9 KB - MD5: d85eaab4a9141a1f343501d3848efc4e
xception_custom.py Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" Python Source Code - 14.0 KB - MD5: c94dea75ef2c5e2eda6a1ac97bfe3ea4
xregexp.js Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" Javascript Code - 226.9 KB - MD5: efda034b953739111c7e06101843860a
xregexp.min.js Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" Javascript Code - 122.3 KB - MD5: b0439563a5d3ea2d52435768fd01238e
zh-CN.js Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" Javascript Code - 768 B - MD5: 2cff662ec5f972b4613566cf5988cda2
zh-TW.js Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" Javascript Code - 707 B - MD5: 04554a227c2ba0f3bb6ca3d2e01e5440
Mask_Dataset.zip Jun 22, 2022 - Handwriting Inpainting Dataset ZIP Archive - 373.6 MB - MD5: 2a552bd2adc7c70b8fb897371e07d558 Contains test and training dataset splits with handwiting masks. The test folder contains the subfolders test00, test05, test10, test15 and test20, with 1000, 1000, 1000, 1000 and 100 masks for the test dataset, respectively. The train folder contains the subfolders train00, train05, train10, train15 and train20, with 100.000, 100.000, 10.000, 10.0...
10_blur.zip Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" ZIP Archive - 4.6 MB - MD5: 4f82bd8d7e56649dc799dc0153f35189
10.zip Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering" ZIP Archive - 3.7 MB - MD5: 0302e172f0cdb8cef6a84f4ecfb31caf

wsj168.png

Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

PNG Image - 1.2 MB -

wsj593.png

Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

PNG Image - 373.9 KB -

xception_custom.py

Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

Python Source Code - 14.0 KB -

xregexp.js

Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

Javascript Code - 226.9 KB -

xregexp.min.js

Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

Javascript Code - 122.3 KB -

zh-CN.js

Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

Javascript Code - 768 B -

zh-TW.js

Sep 13, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

Javascript Code - 707 B -

Mask_Dataset.zip

Jun 22, 2022 - Handwriting Inpainting Dataset

ZIP Archive - 373.6 MB -

Contains test and training dataset splits with handwiting masks. The test folder contains the subfolders test00, test05, test10, test15 and test20, with 1000, 1000, 1000, 1000 and 100 masks for the test dataset, respectively. The train folder contains the subfolders train00, train05, train10, train15 and train20, with 100.000, 100.000, 10.000, 10.0...

10_blur.zip

Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

ZIP Archive - 4.6 MB -

10.zip

Jun 21, 2022 - Data for "VisRecall: Quantifying Information Visualisation Recallability via Question Answering"

ZIP Archive - 3.7 MB -

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