Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes
Table 4: Performance on the Visual Paraphrase task for vis-w2v-coco (left) and vis-w2v-wiki (right).
['vis-w2v-coco Model', 'vis-w2v-coco  [ITALIC] NH', 'vis-w2v-coco Baseline', 'vis-w2v-coco Descs', 'vis-w2v-coco Sents', 'vis-w2v-coco Winds', 'vis-w2v-coco Words']
These descriptions have three sentence each. Observations: In general, PCA configuration outperforms Orig for low complexity models (NH=50). Using entire description or sentences as the context window gives almost the same gains, while performs drops when smaller context windows are used (Winds and Words). As VP is a sentence level task where one needs to reason about the entire sentence to determine whether the given descriptions are paraphrases, these results are intuitive.