Unsupervised Stylish Image Description Generation via Domain Layer Norm
Table 1: Performance comparison between DLN and several baselines. CS, S and T stand for content similarity, SPICE and transfer accuracy. p and r are as defined in Eq. 18. np and nr are the numerator of each. DLN has generally higher score of content related metrics. Higher is better for all metrics except the transfer accuracy.
['Model', 'Data', 'CS', 'S', 'T', '[ITALIC] p', '[ITALIC] r', '[ITALIC] np', '[ITALIC] nr']
We also report p, r and the numerator of each for further comparison. It is worth noting that the perfect transfer accuracy may not be the best since the model could greedily generate the vocabulary used in the target domain and digress from the image content. Therefore, an ideal stylish description is the one with the high content similarity score and an acceptable transfer accuracy. Our DLN consistently outperforms other baselines in term of all semantic related metrics with a marginal drop of transfer accuracy on most datasets. All baselines are better than Random, which suggests all baselines can generate semantic-related description to certain degree. We observe NST has large np and nr in fairy tale. We think this is because NST tends to generate long sentences. For each style (Fairy, Humor, Romance, and Lyrics), the average sentence length of NST is (119,109,103,84) while that of DLN is (38,54,41,97). Therefore, it is possible that NST generates more nouns in the unstylish ground truth. We calculate the BLEU-4, METEOR and CIDEr scores between generated sentences and unstylished ground truth. The DLN has the highest related and lowest unrelated votes while over half of descriptions are voted as unrelated in other baselines. Note that the goal of generated stylish description is not to match every factual aspect of images, it should better be judged whether the description is related to the image if the image appears in the target corpus.