  This report presents our submission to the MS COCO Captioning Challenge 2015.
The method uses Convolutional Neural Network activations as an embedding to
find semantically similar images. From these images, the most typical caption
is selected based on unigram frequencies. Although the method received low
scores with automated evaluation metrics and in human assessed average
correctness, it is competitive in the ratio of captions which pass the Turing
test and which are assessed as better or equal to human captions.
