FIVER @ CVPR 2018

Fine-grained Instructional Video undER standing Workshop

An exploratory workshop focusing on the nascent area of learning from instructional videos. FIVER features topic-focused invited speakers, posters and a discussion panel focused on identifying challenges and future efforts in this area.

Video understanding has advanced quite a long way in the past decade, moving from classifying easily segmented human activity on static backgrounds and tracking single objects smoothly moving without clutter to large-scale detection and segmentation of action amidst dense clutter and translation of video into textual description automatically, to name a few. In this process, the core problems of video understanding have progressed, the raw and annotated data available for these problems has substantially increased, and the suites of methods in study has broadened. However, much of this work remains a proxy for an eventual task or applications, such as video indexing and search, or agent-based understanding of the environment.

In this workshop, we want to take a step beyond these proxies and into a concrete and grounded tasks: learning from instructional video. This is a nascent area in the vision, learning, robotics and broader AI communities with but a handful of recent papers and datasets being published on the topic. The goal of this workshop is to start a conversation around learning from instructional video with the ultimate plan to organize a future, longer-scale workshop with a challenge associated with the problem area. In this conversation, we invite abstract submissions, have invited speakers, present an overview of the problem area (by organizers) and have a panel discussion that will ask and discuss questions like: What are the core problems in learning from instructional video? What are reasonable goals to set for this space in the next few years? What data do we have available now and what data do we need?

Schedule

To be posted when available.

Abstracts

To be posted when available.

Organizers

  • Jason Corso, University of Michigan
  • Ivan Laptev, INRIA
  • Josef Sivic, INRIA and Czech Technical University
  • Luowei Zhou, University of Michigan

Awards & Certifications

  • Google Analytics Certified Developer
  • Mobile Web Specialist - Google Certification
  • 1st Place - University of Colorado Boulder - Emerging Tech Competition 2009
  • 1st Place - University of Colorado Boulder - Adobe Creative Jam 2008 (UI Design Category)
  • 2nd Place - University of Colorado Boulder - Emerging Tech Competition 2008
  • 1st Place - James Buchanan High School - Hackathon 2006
  • 3rd Place - James Buchanan High School - Hackathon 2005