Learning from PhotoShop Operation Videos: The PSOV Dataset

Jingchun Cheng, Han Kai Hsu, Chen Fang, Hailin Jin, Shengjin Wang, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we present the PhotoShop Operation Video (PSOV) dataset, a large-scale, densely annotated video database designed for the development of software intelligence. The PSOV dataset consists of 564 densely-annotated videos for Photoshop operations, covering more than 500 commonly used commands in the Photoshop software. Videos in this dataset are obtained from YouTube, manually watched and annotated precisely to seconds by experts. There are more than 74 h of videos with 29,204 labeled commands. To the best of our knowledge, the PSOV dataset is the first large-scale software operation video database with high-resolution frames and dense annotations. We believe that this dataset can help advance the development of intelligent software, and has extensive application aspects. In this paper, we describe the dataset construction procedure, data attributes, proposed tasks and their corresponding evaluation metrics. To demonstrate that the PSOV dataset has sufficient data and labeling for data-driven methods, we develop a deep learning based algorithm for the command classification task. We also carry out experiments and analysis with the proposed method to encourage better understanding and usage of the PSOV dataset.

Original languageEnglish
Title of host publicationComputer Vision – ACCV 2018 - 14th Asian Conference on Computer Vision, Revised Selected Papers
EditorsGreg Mori, Hongdong Li, C.V. Jawahar, Konrad Schindler
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783030208691
Publication statusPublished - 2019
Event14th Asian Conference on Computer Vision, ACCV 2018 - Perth, Australia
Duration: 2018 Dec 22018 Dec 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11364 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th Asian Conference on Computer Vision, ACCV 2018

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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