# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 4 Experiments
### Ablation Study  
To verify the validity of each design in MGSPU, we conducted ablation experiments on the MIRFLICKR-25K and NUS-WIDE datasets, serveral variations were considered for this purpose:  
* **MGSPU-1:** it removes semantic similarity matrix updating from MGSPU.
* **MGSPU-2:** it removes the similarity consistency reconstruction from MGSPU.
* **MGSPU-3:** it modifies similarity consistency reconstruction by replacing inter-modal reconstruction with intra-modal reconstruction.
* **MGSPU-4:** it removes the GCN module from MGSPU.  
From Tables 8 and 9, the following observations can be obtained: firstly, the comparison of MGSPU-1 with our the full MGSPU method verifies that the proposed dual instruction fusion updating strategy can improve the quality of instance similarity matrix to enhance the retrieval performance. Specifically, the retrieval accuracy of MGSPU-1 for both I2T and T2I task show decrease in some extent: on MIRFLICKR-25K, mAP@50 results of I2T task drop from 0.898 (16 bits), 0.915 (32 bits), 0.927 (64 bits), 0.936 (128 bits) to 0.894 (16 bits), 0.912 (32 bits), 0.924 (64 bits), 0.933 (128 bits), respectively; mAP@50 results of T2I task drop from 0.876 (16 bits), 0.883 (32 bits), 0.889 (64 bits), 0.900 (128 bits) to 0.872 (16 bits), 0.876 (32 bits), 0.883 (64 bits), 0.892 (128 bits), respectively. It indicates that without the semantic similarity matrix updating, complex similarity relationship learning suffers from disturbance by noise. Secondly, we can clearly observe that MGSPU-2 has a remarkably performance degradation compared with the full version of MGSPU. This phenomenon confirms that similarity consistency reconstruction is beneficial to preserve semantic information into hash code. Thirdly, with intra-modal reconstruction, MGSPU-3 performs better than MGSPU-2 especially on long hash codes (e.g., 64 or 128 bits). However, compared with the inter-modal reconstruction used in our method, the performance of MGSPU-3 is slightly weaker, which indicates that the inter-modal reconstruction is more helpful to reduce the heterogeneity between the original feature and the hash code. Lastly, after removing GCN module, MGSPU-4 achieves lower retrieval accuracy than ours. these results show that the structural similarity aggregated from neighborhoods by the GCN module is essential to enrich the similarity relationship information of each instance.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{3}{*}{**Methods**} & \multicolumn{3}{c}{**MIFRLICKR-25K**} \\ \cline{2-9}  & **I2T** & & & & **T2I** & & & \\ \cline{2-9}  & **16 bits** & **32 bits** & **64 bits** & **128 bits** & **16 bits** & **32 bits** & **64 bits** & **128 bits** \\ \hline MGSPU-1 & 0.894 & 0.912 & 0.924 & 0.933 & 0.872 & 0.876 & 0.883 & 0.892 \\ MGSPU-2 & 0.886 & 0.902 & 0.909 & 0.913 & 0.849 & 0.857 & 0.866 & 0.876 \\ MGSPU-3 & 0.897 & 0.913 & 0.926 & 0.934 & 0.872 & 0.885 & 0.889 & 0.894 \\ MGSPU-4 & 0.895 & 0.904 & 0.924 & 0.926 & 0.868 & 0.873 & 0.886 & 0.887 \\
**Ours** & **0.898** & **0.915** & **0.927** & **0.936** & **0.876** & **0.883** & **0.889** & **0.900** \\ \hline \hline \end{tabular}
\end{table}
Table 8: mAP@50 score for ablation study on MIRFLICKR-25K.