Procedural Human Action Videos - Instance Segmentation
Cesar Roberto de Souza and Adrien Gaidon and Yohann Cabon and Antonio Manuel Lopez Pena

phav_v2-instancegt (318 files)
instancegt_5009.tar.bz2 44.29MB
instancegt_5007.tar.bz2 45.70MB
instancegt_5008.tar.bz2 47.11MB
instancegt_5005.tar.bz2 47.45MB
instancegt_5006.tar.bz2 42.47MB
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instancegt_5004.tar.bz2 47.97MB
instancegt_5001.tar.bz2 42.70MB
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instancegt_4023.tar.bz2 43.67MB
instancegt_5000.tar.bz2 45.53MB
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instancegt_4009.tar.bz2 47.13MB
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instancegt_4008.tar.bz2 42.93MB
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instancegt_3111.tar.bz2 49.23MB
instancegt_3112.tar.bz2 49.21MB
instancegt_3109.tar.bz2 44.26MB
instancegt_3110.tar.bz2 43.87MB
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instancegt_3105.tar.bz2 49.45MB
instancegt_3106.tar.bz2 43.31MB
instancegt_3099.tar.bz2 49.87MB
instancegt_3100.tar.bz2 48.15MB
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Type: Dataset
Tags: deep learning, action recognition, procedural generation, video classification

title= {Procedural Human Action Videos - Instance Segmentation},
keywords= {Action recognition, procedural generation, deep learning, video classification},
journal= {},
author= {Cesar Roberto de Souza and Adrien Gaidon and Yohann Cabon and Antonio Manuel Lopez Pena},
year= {2017},
url= {},
license= {Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)},
abstract= {Procedural Human Action Videos (PHAV)
 This torrent contains the Procedural Human Action Videos (PHAV) dataset. This 
 version of the dataset contains 39,982 videos that have been generated by a 
 computer. The dataset is made available under the Creative Commons BY-NC-SA
 (Attribution-NonCommercial-ShareAlike 4.0 International) license. This sub-
 directory contains the Pixel-wise Semantic Segmentation Instance Ground Truth
 data modality for these videos.
 A human-readable version of the license terms is available at:
 The full license terms are available at:
 By using PHAV, you agree to abide to the terms of the Creative Commons BY-NC-SA
 license. If you do not agree with the terms of this license, please contact the
 dataset authors for other licensing options.
 For more information, including details about how to parse the different files
 included in this release, how to use them in your experiments, and how to cite
 the PHAV dataset in your academical paper, please visit:

superseded= {},
terms= {By using PHAV, you agree to abide to the terms of the Creative Commons BY-NC-SA license.}

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