Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes
Table 3: Performance on text-based image retrieval. R@x: higher is better, medR: lower is better
['Approach', 'R@1 (%)', 'R@5 (%)', 'R@10 (%)', 'med R']
We next present results on the text-based image retrieval task (Sec. This task requires visual grounding as the query and the ground truth tuple can often be different by textual similarity, but could refer to the same scene (Fig. As explained in Sec. Table. Note that vis-w2v here refers to the embeddings learnt using the CS dataset. We find that the best performing models are vis-w2v-wiki (shared) (as per R@1, R@5, medR) and vis-w2v-coco (separate) (as per R@10, medR). These get Recall@10 scores of ≈49.5% whereas the baseline w2v-wiki and w2v-coco embeddings give scores of 45.4% and 47.6%, respectively.