Competition

Task

The challenge task is the semi-supervised video object segmentation, which targets at segmenting a particular object instance throughout the entire video sequence given only the object mask of the first frame. Different from previous video object segmentation challenges in CVPR 2017 and 2018, we will provide much larger scale of training and test data to foster various kinds of algorithms. In addition, our test dataset will have unseen categories which do not exist in the training dataset, in order to evaluate the generalization ability of algorithms.

Dataset

Our dataset contains three subsets.

• Training: 3471 video sequences with densely-sampled multi-object annotations. Each object is annotated with a category name, there is 65 categories in training set.
• Validation: 507 video sequences with the first-frame annotations. It includes objects from the 65 training categories, and 26 unseen categories in training.
• Test: Another 541 sequences with the first-frame annotations. It includes objects from the 65 training categories, and 29 unseen categories in training.
• RGB images and annotations for the labeled frames will be provided. We will also provide a download link for all image frames. Evaluation of validation and test sets will be done by uploading results to our evaluation server. Category information for validation and test sets will not be released.

Evaluation Criteria

Similar to a previous video object segmentation challenge DAVIS, we will be using Region Jaccard (J) and Boundary F measure (F) as evaluation metric. The overall ranking measures will be computed in the following way:

• 1. compute J and F for both seen and unseen categories, averaged over all corresponding objects.
• 2. the final score is the average of the four scores: J for seen categories, F for seen categories, J for unseen categories, and F for unseen categories.

Note that we have some of the objects start appearing from the middle of videos, we will only compute the metrics from the first occurrence of these objects to the end of the video.

Terms and Conditions

The annotations in this dataset belong to the organizers of the challenge and are licensed under a Creative Commons Attribution 4.0 License.

The data is released for non-commercial research purpose only.

The organizers of the dataset as well as their employers make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the organizers, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted videos that he or she may create from the Database. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions. The organizers reserve the right to terminate Researcher's access to the Database at any time. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer.