Contents
Table 6.8: Flickr30K experiments with bigram model and binary inputs. 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: n-gram  [BOLD] Metric', '[BOLD] our model: n-gram  [BOLD] Image Annotation', '[BOLD] our model: n-gram  [BOLD] Image Annotation', '[BOLD] our model: n-gram  [BOLD] Image Annotation', '[BOLD] our model: n-gram  [BOLD] Image Annotation', '[BOLD] our model: n-gram  [BOLD] Image Search', '[BOLD] our model: n-gram  [BOLD] Image Search', '[BOLD] our model: n-gram  [BOLD] Image Search', '[BOLD] our model: n-gram  [BOLD] Image Search']
Finally we select the best performing model as having 1,000 unit hidden layers in both textual and trainable visual models, and also 1,000 unit multimodal embedding vector. We reached the best performance using a 300 dimensional embedding (linearly connected with 5,000 input units). The small trainable visual network also does not contain any hidden layer as well, and the 4,096 dimensional OverFeat feature vectors are linearly transformed into the 300 embedding dimension.