# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 4 Experiments
### Performance Evaluation  
We compare the proposed method with nine baselines on MIRFLICKR-25K and NUS-WIDE datasets. The performance of all these methods are evaluated by Hamming Ranking protocol and hash lookup protocol. Tables 6 and 7 illustrates the mAP@50 results of our method and the competitors varying hash code lengths (16, 32, 64, 128 bits) on MIRFLICKR  
**Hamming Ranking. ** It is clearly from the Tables 6 and 7 that the proposed method performs better than the baselines. Specifically, on MIRFLICKR-25K, we found that our method achieved the highest mAP@50 score for both retrieval tasks (I2T: mAP@50 = 0.898 (16 bits), mAP@50 = 0.915 (32 bits), mAP@50 = 0.927 (64 bits), mAP@50 = 0.936 (128 bits); T2I: mAP@50 = 0.876 (16 bits), mAP@50 = 0.883 (32 bits), mAP@50 = 0.889 (64 bits), mAP@50 = 0.900 (128 bits)). For example, our method beats out the strongest competitor, DRNPH, by a significant margin on both two tasks, especially in shorter hash code length: 0.022 (16 bits), 0.013 (32 bits), 0.013 (64 bits) on I2T task, and 0.016 (16 bits), 0.011 (32 bits) on T2I task. The reason behind these results is obvious: with the engagement of the proposed similarity matrix updating strategy, our method can gradually eliminate the noise of the original features used in the similarity relationship construction so as to improve similarity consistency preserving, which is unfortunately ignored by DRNPH. In all but a few case (32 bits code length on I2T and I2T task, MGSPU was defeated by AGCH and DSAH marginally), our method won the competition again on NUS-WIDE by stand-out performance: mAP@50 = 0.811 (16 bits), 0.826 (32 bits), 0.844 (64 bits), 0.858 (128 bits) on I2T task, and mAP@50 = 0.780 (16 bits), 0.786 (32 bits), 0.806 (64 bits), 0.813 (128 bits) on T2I task. Compared with mainstream solutions, complex similarity correlations can be greatly mined through the semantic similarity matrix update strategy, and the MGSP module further retains the potential similarity structure between data. **Hash Lookup. ** To comprehensively showcase the comprehensive performance comparison of MGSPU with baselines, we draw P-R curves in Figures 5 and 6 with different code lengths on both two datasets. As expected, in addition to dramatically defeating hand-crafted feature based method CMFH, MGSPU outperforms state-of-the-art competitors DRNPH, DSAH and AGCH on various hash code length. This observation is mainly due to the search performance boosting from the interplay of similarity updating and multi-grained similarity preserving of hash codes. **Discussion. ** It is no secret that, the main reason of poor performance of early models (such as CMFH and DBRC) is their shallow feature extraction techniques that cannot obtain feature representations with rich semantic information. With the help of powerful deep learning techniques, deep neural networks based methods such as DJSRH, JDSHH, AGCH and DAEH achieved good results. Among them, DJSRH is equiped with a reconstruction framework for training, which is more competitive than batch training. AGCH uses GCN to aggregate neighborhood information and enhance feature expression. DAEH leverages teacher networks to enhance weaker hashing networks. However, all of them build sim  
Figure 6: P-R curves of different models at different lengths on MIRFLICKR-25K dataset. (a) I2T:16 bit. (b) I2T:32 bit. (c) I2T:64 bit. (d) I2T:128 bit. (e) T2I:16 bit. (f) T2I:32 bit. (g) T2I:64 bit. (h) T2I:128 bit. ilarity matrices based on original features, which inevitably bring noises into semantic relationships so as to introduce biases. Furthermore, these methods either maintain local or global similarities to preserve the semantic relationships. For example, the similarity matrix in DJSRH contains redundant information from intra-modal fusion items, while DAEH ignores the semantic relationships of intra-modal details.