To investigate how the image representations affect the final performance, we compare the ancestor-F1 score when different pre-trained CNNs are used for visual feature extraction.
we employ both the CNN-128 model (128 dimensional feature with 15.6% top-5 error on ILSVRC12) and the VGG-16 model (4096 dimensional feature with 7.5% top-5 error) by Simonyan and Zisserman (2014), but only observe a slight improvement of 0.01 on the ancestor-F1 score for the later one.