Large-Scale Video Classification Challenge

23 Oct. 2017, ACM Multimedia, Mountain View, CA, USA

Welcome to the website of the Large-Scale Video Classification Challenge workshop. Recognizing visual contents in unconstrained videos has become a very important problem for many applications, such as Web video search and recommendation, smart advertising, robotics, etc. This workshop and challenge aims at exploring new challenges and approaches for large-scale video classification with large number of classes from open source videos in a realistic setting, based upon an extension of Fudan-Columbia Video Dataset (FCVID).

This newly collected dataset contains over 8000 hours of video data from YouTube and Flicker, annotated into 500 categories. The categories cover a wide range of popular topics like social events (e.g., “tailgate party”), procedural events (e.g., “making cake”), objects (e.g., “panda”), scenes (e.g., “beach”), etc. Compared with FCVID, new categories are added to enrich the original hierarchy. For example, 76 new categories are added to "cooking" totaling 93 classes, and 75 new classes are added to "sports". During annotation, multiple labels have been considered as much as possible for each video. When labeling a particular category, categories that are not likely to co-occur are filtered out manually with the remaining labels considered for annotation.

The following components will be publicly available under this challenge:

  • Training Set: over 62,000 temporally untrimmed videos from 500 classes. We also provide pre-extracted features and frames (1 fps).
  • Validation Set: around 15,000 videos with annotations of classes.
  • Test Set: over 78,000 temporally untrimmed videos with withheld ground truth.

We will evaluate the success of the proposed methods based on mean Average Precision (mAP) across all categories. Participants may either submit a notebook paper that briefly describes their system, or a research paper detailing their approach. Notebook papers submitted before Aug. 15 will be included in the workshop proceedings.

Awards will be given based on evaluation performance, and the top performers will be invited to give oral presentations at the workshop.


Evaluation Server is now online! Please follow the instructions to submit the results. You are strongly advised to get familiar with the submission process using validation set in the model development phase.

Downloading links have been sent out.

Please email us (zxwu AT your affiliation information in order to receive the link for downloading frames and pre-extracted features. If you need original videos (around 3T) for training, please attach a scanned copy of the signed Agreement form in the email, we will then send you the download instructions at our discretion.

If you find the data useful or want to cite the challenge, please use the following reference:

   author = "Wu, Zuxuan and Jiang, Y.-G. and Davis, Larry S and Chang, Shih-Fu",
   title = "{LSVC2017}: Large-Scale Video Classification Challenge",
   howpublished = "\url{}",
   Year = {2017}} 

Important dates

23 July Development kit, training and validation subsets, Testing data without ground-truth annotations will be available.
15 August Evaluation server will be available. Notebook submission deadline (optional).
18 September Submission Deadline
20 September Winners Announcement
23 October Workshop Presentation

Paper submission

Submissions may be up to 8 pages long, plus one additional page for references (i.e., the references-only page is not counted to the page limit of 8 pages), formatted according to ACM MM 2017 guidelines for regular papers (using the acm-sigconf template which can be obtained from the  ACM proceedings style page).

Submission site Please select the track "Large-Scale Video Classification Challenge"

About external data

External data can be used to train the algorithms, however each submission should explicitly cite the data used for model training.


Zuxuan Wu University of Maryland
Yu-Gang Jiang Fudan University
Larry Davis University of Maryland
Shih-Fu Chang Columbia University