Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes
Table 1: Performance on the common sense task of [34]
['Approach', 'common sense AP (%)']
We first present our results on the common sense assertion classification task (Sec. We use NK=25, which gives the best performance on validation. We handle tuple elements, tP, tR or tS, with more than one word by placing each word in a separate window (i.e. |Sw|=1). For instance, the element “lay next to” is trained by predicting the associated visual context thrice with “lay”, “next” and “to” as inputs. We achieve larger gains (5.8%) with vis-w2v-wiki over w2v-wiki. Thus, this is an instance where vis-w2v (learnt from abstract scenes) generalizes to text describing real images.