  We hypothesize that end-to-end neural image captioning systems work seemingly
well because they exploit and learn `distributional similarity' in a multimodal
feature space by mapping a test image to similar training images in this space
and generating a caption from the same space. To validate our hypothesis, we
focus on the `image' side of image captioning, and vary the input image
representation but keep the RNN text generation component of a CNN-RNN model
constant. Our analysis indicates that image captioning models (i) are capable
of separating structure from noisy input representations; (ii) suffer virtually
no significant performance loss when a high dimensional representation is
compressed to a lower dimensional space; (iii) cluster images with similar
visual and linguistic information together. Our findings indicate that our
distributional similarity hypothesis holds. We conclude that regardless of the
image representation used image captioning systems seem to match images and
generate captions in a learned joint image-text semantic subspace.
