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Table 6.4: Flickr30K experiments with unigram BoW model and Word2Vec initialization. R@K is Recall@K (high is good). Med r is the median rank (low is good). rPrecision(5) is the precision at rank 5 (high is good).
['[BOLD] our model: BoW  [BOLD] Metric', '[BOLD] our model: BoW  [BOLD] Image Annotation', '[BOLD] our model: BoW  [BOLD] Image Annotation', '[BOLD] our model: BoW  [BOLD] Image Annotation', '[BOLD] our model: BoW  [BOLD] Image Annotation', '[BOLD] our model: BoW  [BOLD] Image Search', '[BOLD] our model: BoW  [BOLD] Image Search', '[BOLD] our model: BoW  [BOLD] Image Search', '[BOLD] our model: BoW  [BOLD] Image Search']
The hidden layer of textual model is fixed to 300 units, since the word2vec embeddings have dimensionality of 300. We fixed the hidden layer of the trainable visual model to 1,000 units, and the size of multimodal embedding vector is also fixed to 1,000. A quick comparison between the performance metrics in these two tables shows that although word2vec initialization does help in improving the accuracy of the system, but the effects are not extremely drastic. For example R@10:avg_txt for image annotation increases by 3.3% while for image search increases by 3.86%.