HACS Temporal Action Localization Challenge - Weakly-supervised Learning Track

Organized by zhaohang0124 - Current server time: Oct. 25, 2020, 7:22 p.m. UTC

First phase

Final
April 13, 2020, midnight UTC

End

Competition Ends
June 3, 2020, 11:59 p.m. UTC

Challenge Overview

The goal of this challenge is to temporally localize actions in untrimmed videos. We will host HACS Temporal Action Localization Challenge in the CVPR'20 International Challenge on Activity Recognition Workshop.
More information can be found at HACS Challenge 2020 site.

Weakly-supervised Learning Track

For this track, participants are allowed to use two datasets for the temporal action localization task, namely HACS Clips and HACS Segments. HACS Segments contains videos densely annotated with temporal action segments, , while HACS Clips contains videos where only a sparse set of short video clips are annotated.. These two datasets share the same video source and taxonomy. Participants are encouraged to explore a weakly-supervised training procedure to learn action localization models. The following two dataset are allowed for model training, and testing will be performed on the test set of HACS Segments:

HACS Clips

  • 0.5M videos where 1.55M video clips of 2-second duration are sampled.
  • Video clips are annotated with either one label out of 200 action classes or background label.

HACS Segments

  • Temporal annotations on action segment type, start time, end time.
  • 200 action classes, nearly 140K action segments annotated in nearly 50K videos.
  • 37.6Ktraining videos, 6K validation videos, 6K testing videos.

Important Dates

  • March 1, 2020: Challenge is announced, Train/Val/Test sets are made available.
  • April 13, 2020: Evaluation server opened.
  • May 29, 2020: Evaluation server closed.
  • June 1, 2020: Deadline for submitting the report.
  • June 14, 2020: Full-day challenge workshop at CVPR 2020.

Evaluation Metric

We use mAP as our evaluation metric, which is the same as ActivityNet localization metric.

Interpolated Average Precision (AP) is used as the metric for evaluating the results on each activity category. Then, the AP is averaged over all the activity categories (mAP). To determine if a detection is a true positive, we inspect the temporal intersection over union (tIoU) with a ground truth segment, and check whether or not it is greater or equal to a given threshold (e.g. tIoU > 0.5). The official metric used in this task is the average mAP, which is defined as the mean of all mAP values computed with tIoU thresholds between 0.5 and 0.95 (inclusive) with a step size of 0.05.

Submission Format

You should submit a JSON file (and then ZIP into .zip) in the following format, where each video ID has a list of predicted action segments. Submission portal will be available on August 1st.

{
  "results": {
    "--0edUL8zmA": [
      {
        "label": "Dodgeball",
        "score": 0.84,
        "segment": [5.40, 11.60]
      },
      {
        "label": "Dodgeball",
        "score": 0.71,
        "segment": [12.60, 88.16]
      }
    ]
  }
}

Challenge Rules

You may submit up to once a day and 30 times total.

Final

Start: April 13, 2020, midnight

Description: Challege Phase: Please ZIP your .json file to .zip for submission. The results on the test set will be revealed when the organizers make them available.

Competition Ends

June 3, 2020, 11:59 p.m.

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