From the webpage:
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The goal of this challenge is to solve simultaneously ten image classification problems representative of very different visual domains. The data for each domain is obtained from the following image classification benchmarks:
- ImageNet [6].
- CIFAR-100 [2].
- Aircraft [1].
- Daimler pedestrian classification [3].
- Describable textures [4].
- German traffic signs [5].
- Omniglot. [7]
- SVHN [8].
- UCF101 Dynamic Images [9a,9b].
- VGG-Flowers [10].
The union of the images from the ten datasets is split in training, validation, and test subsets. Different domains contain different image categories as well as a different number of images.
The task is to train the best possible classifier to address all ten classification tasks using the training and validation subsets, apply the classifier to the test set, and send us the resulting annotation file for assessment. The winner will be determined based on a weighted average of the classification performance on each domain, using the scoring scheme described below. At test time, your model is allowed to know the ground-truth domain of each test image (ImageNet, CIFAR-100, …) but, of course, not its category.
It is up to you to make use of the data, and you can either train a single model for all tasks or ten independent ones. However, you are not allowed to use any external data source for training. Furthermore, we ask you to report the overall size of the model(s) used.
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The competition is over but you can continue to submit results and check the results in the leaderboard. (There’s an idea that merits repetition.)
Will this be your entertainment game for the holidays?
Enjoy!