1. Call for Papers

The ability to craft and understand stories is a crucial cognitive tool used by humans for communication. According to computational linguists, narrative theorists and cognitive scientists, story understanding is a good proxy to measure the readers' intelligence. Readers can understand a story as a way of problem-solving in which, for example, they keep focusing on how main characters overcome certain obstacles throughout the story. Readers need to make inferences both in prospect and in retrospect about the causal relationships between different events in the story.

Video story data such as TV shows and movies can serve as an excellent testbed to evaluate human-level AI algorithms from two points of view. First, video data has different modalities such as a sequence of images, audio (including dialogue, sound effects and background music) and text (subtitles or added comments). Second, video data shows various cross-sections of everyday life. Therefore, understanding video stories can be thought of as a significant challenge to current AI technology, which involves analyzing and simulating human vision, language, thinking, and behavior.

This workshop aims to invite experts from various fields, including vision, language processing, multimedia, and speech recognition, to provide their perspective on existing research, and initiate discussion on future challenges in data-driven video understanding. To assess current state-of-the-art methodologies and encourage rapid progress in the field, we will also host a challenge based on the DramaQA dataset, which encourages story-centered video question answering. Topics of interest include but are not limited to:

  • Deep learning architectures for multi-modal video story representation
  • Question answering for video stories
  • Summarization and retrieval from long story video contents
  • Scene description generation for video understanding
  • Activity/event recognition from video
  • Character identification & interaction modeling in video
  • Scene graph generation and relationship detection from video
  • Emotion recognition in video
  • Novel tasks about video understanding and challenge dataset

  • This workshop will invite leading researchers from various fields. Details of submission instruction will be anounced soon.


    2. Important Dates

  • Signup to receive updates: link
  • Paper Submission Deadline: Late May (TBD)
  • Workshop Date: August 28, 2020 (Full day)
  • Details will be announced soon.


  • 3. Organizers

    person
    Yu-Jung Heo
    Seoul National University
    person
    Seongho Choi
    Seoul National University
    person
    Kyoung-Woon On
    Seoul National University
    person
    Minsu Lee
    Seoul National University
    person
    Vicente Ordóñez Román
    University of Virginia
    person
    Leonid Sigal
    University of British Columbia
    person
    Chang Dong Yoo
    KAIST
    person
    Gunhee Kim
    Seoul National University
    person
    Marcello Pelillo
    University of Venice
    person
    Byoung-Tak Zhang
    Seoul National University


    Back to top

    © 2019 Video Intelligence Center @ Seoul National University