Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes
Table 2: Performance on visual paraphrasing task of [23].
['Approach', 'Visual Paraphrasing AP (%)']
We next describe our results on the Visual Paraphrasing (VP) task (Sec. The task is to determine if a pair of descriptions are describing the same scene. Each description has three sentences. Table. We vary the size of the context window Sw and check performance on the VAL set. We obtain best results with the entire description as the context window Sw, NH=200, and NK=100. Our vis-w2v models give an improvement of 0.7% on both w2v-wiki and w2v-coco respectively. Their combined text + imagination model performs 0.2% better (95.5%) than our model. Note that our approach does not have the additional expensive step of generating an imagined visual scene for each instance at test time. Qualitative examples of success and failure cases are shown in Fig.