# Multi-Label Weighted Contrastive Cross-Modal Hashing
## 4 Experiment
### Performance Comparisons and Discussion  
We investigate the retrieval performance of the proposed method MLWCH by comparing it with several state-of-the-art baselines on the MIRFLICKR-25K, NUS-WIDE, and MS COCO datasets. In the following, we discuss the comparison via Hamming ranking and hash lookup. **Hamming Ranking. ** The MAP@50 of MLWCH and baseline methods under distinct hash code lengths 16 bits, 32 bits, and 64 bits are listed in Tables 2-4. From the experimental results, we have the following findings:  
* The proposed method MLWCH has shown remarkable performance on all benchmark datasets: it beats both the hand-crafted methods and the deep neural network-based methods in all cases. Particularly, our method outperforms SCCGDH, the strongest competitors, by a significant margin. For the I2T task, the results were 0.0339 (16 bits), 0.0392 (32 bits), and 0.0524 (64 bits) on MIRFLICKR-25K; 0.0335 (16 bits), 0.0174 (32 bits), and 0.0364 (64 bits) on NUS-WIDE; and 0.0755 (16 bits), 0.0499 (32 bits), and 0.0495 (64 bits) on MS COCO. For the T2I task, the results were 0.0708 (16 bits), 0.0425 (32 bits), and 0.0386 (64 bits) on MIRFLICKR-25K; 0.0217 (16 bits), 0.0231 (32 bits),and 0.0268 (64 bits) on NUS-WIDE; and 0.0609 (16 bits), 0.0801 (32 bits), and 0.0609 (64 bits) on MS COCO. These outstanding results verify that integrating compact consistent similarity representation with multi-label weighted contrastive learning can effectively enhance the performance of cross-modal hashing retrieval. * It is clear to see that the deep hashing methods achieve superior retrieval performances than traditional shallow hashing methods in most cases on the three datasets. The main reason may be that deep learning methods can extract more essential high-level features than traditional shallow methods, which effectively reduces the semantic gap between modalities. * It can be found that other than the proposed method, all these baselines also achieve relatively lower results on MS COCO than the other two datasets. This observation is mainly due to the fact that the MS COCO dataset provides more label categories than the two other datasets, which in other words brings greater challenges to cross-modal hash learning. Nevertheless, MLWCH still achieves the best results, which corroborates that the proposed technique could efficiently capture cross-modal semantic consistency in complex semantic conditions. * Compared to deep cross-modal hashing methods utilizing multi-labels, our proposed method MLWCH still obtains the highest performance. Particularly, it in all cases greatly defeats Bi_NCMH, which uses bi-direction relation reasoning. The superiority of MLWCH is partly due to the fact that the proposed technique realizes a more accurate multi-label similarity consistency reasoning to calculate the semantic relevance of original instances. In addition, the combination of multi-label learning with supervised contrastive learning can effectively minimize the heterogeneity gap of original instances, which delivers superior hash learning performance. **Hash Lookup. ** By varying the Hamming radius from 0 to \(k\), we plot the PR curves for hash code lengths of 16 bits, 32 bits, and 64 bits on the MIRFLICKR-25K, NUS-WIDE, and MS COCO datasets, respectively. These curves are depicted in Figures 4-6. It is obvious that, in all cases, the PR curves of the proposed method are evidently higher than those of all baselines, which verifies that MLWCH can learn cross-modal semantic relationships more efficaciously than the prevailing solutions.