From Visual Attributes to Adjectivesthrough Decompositional Distributional Semantics
Table 3: Summary of training and evaluation sets.
['[EMPTY]', 'Training #im.', 'Training #attr.', 'Training #obj.', 'Evaluation #im.', 'Evaluation #attr.', 'Evaluation #obj.']
To further maximize the amount of training data points, we conduct a leave-one-attribute-out evaluation, in which the cross-modal mapping function is repeatedly learned on all 72 attributes from the training set, as well as all but one attribute from the evaluation set This results in 72+(25−1)=96 training attributes in total. On average, 45 images per attribute are used. The performance is measured for the single attribute that was excluded from training. We thus try to induce attribute-denoting adjective labels by exploiting only widely-available object-noun data. We compare it with standard zero-shot learning using direct label retrieval as well as against a number of challenging alternatives that exploit gold-standard information about the depicted objects.