Visual Coreference Resolution in Visual Dialog using Neural Module Networks
Table 4: Retrieval performance on the test-standard split of VisDial v1.0 dataset [13] (discriminative models). Higher the better for mean reciprocal rank (MRR), recall@k (R@1, R@5, R@10), and normalized discounted cumulative gain (NDGC) while lower the better for mean rank. Our CorefNMN model outperforms all other models across all metrics, except neural module baseline (NMN) on NDGC.
['[BOLD] Model', '[BOLD] MRR', '[BOLD] R@1', '[BOLD] R@5', '[BOLD] R@10', '[BOLD] Mean', '[BOLD] NDCG']
Performance on VisDial v1.0 is given in Tab. Our CorefNMN outperforms all other approaches on all metrics, except the neural module baseline (NMN) on the NDCG metric. (NDCG) from team DL-61, but the approach and other details (e.g. features, use of an ensemble) are not fully known and unpublished at this point in time.