Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images
Table 1: The number of images for the three datasets.
['[EMPTY]', 'Train', 'NC Test', 'Validation']
We randomly separate the above three datasets into training, testing and validation sets. To investigate the possible overfitting issues on these datasets, in the testing stage, we randomly picked images from the testing set of the Base Set and treated them as a separate set of testing images. The number of added images is equal to the size of the original test set (e.g. 1000 images are picked for NewObj-Cat testing set). We denote the original new concept testing images as Novel Concept (NC) test set and the added base testing images as Base test set. A good novel visual concept learning method should perform better than the base model on NC test set and comparable on Base test set.