Moments in Time Challenge 2018

Register to participate in the Moments in Time Recognition Challenge at CVPR'18, jointly held with the ActivityNet Challenge 2018. The goal of this challenge is to identify the event labels depicted in a 3 second video. The video data comes from the Moments in Time dataset, which can be downloaded here . The challenge has two tracks:

Full Track

A classification task on the entire Moments in Time dataset:

  • 339 classes
  • 802,264 training videos
  • 33,900 validation videos
  • 67,800 testing videos
Mini Track

A classification task for students on a subset of Moments in Time dataset:

  • 200 classes
  • 100,000 training videos
  • 10,000 validation videos
  • 20,000 testing videos

The Mini track is open only to students (all the team members should be registered students, the team advisor/coach could be faculty members associated with a university/college). Data for baselines for the Mini track will be made available soon.

Important Dates:

  • March 1, 2018: Training data and development kit with evaluation scripts made available.
  • April 1, 2018: Testing videos are released.
  • June 1, 2018: Submission deadline.
  • June 7, 2018: Challenge results released.
  • June 22, 2018: Winner(s) are invited to present at the Workshop.

Evaluation Metric

We will use top-k accuracy on the testing set as the official metrics for this task. For each video, an algorithm will produce $k$ labels $l_{j}$, $j = 1,..,k$. The ground truth label for the video is $g$. The error of the algorithm for that video would be: $$e = \min_{j} d(l_{j}, g),$$ with $d(x,y) = 0$ if $x=y$ and $1$ otherwise. The overall error score for an algorithm is the average error over all videos. We will use $k=1$ and $k=5$ and the winner of the challenge will be selected based on the average of these two errors.

Submission Format

When submitting your results for the challenge, please provide a plain text file containing one video prediction per line. Each line should contain the video filename followed by up to 5 detected class labels, sorted by confidence in descending order:

<filename> <class(1)> <class(2)> <class(3)> <class(4)> <class(5)>