HUSE: Hierarchical Universal Semantic Embeddings
Table 1: Retrieval performance of various methods on four retrieval tasks of UPMC Food-101 dataset. The entries above the dashed line correspond to the methods without semantic information and the entries below it correspond to methods that incorporate semantic information. The best value per column is set in bold font.
['[EMPTY]', 'Image2Image R@1', 'Image2Image R@5', 'Image2Image R@10', 'Image2Text R@1', 'Image2Text R@5', 'Image2Text R@10', 'Text2Image R@1', 'Text2Image R@5', 'Text2Image R@10', 'Text2Text R@1', 'Text2Text R@5', 'Text2Text R@10']
results on 4 retrieval tasks. The results show that HUSE is outperforming all the baselines across all retrieval tasks in all measures on UPMC Food-101 dataset. The more evident gains on cross-modal retrieval tasks compared to in-modal retrieval show that HUSE is able to address media gap more effectively than other methods. Moreover, the significant gains on R@1 metric compared to R@5, R@10 metrics show that HUSE is learning instance level semantics better than the other baselines. In general, the methods that uses the semantic information are performing better than the methods that are not using semantic information. This shows that the semantic information is important even when class level retrieval tasks are considered.