# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 4 Experiments
### Baselines and Implementation Details  
**Baselines.** we compare the proposed MGSPU method with nine baselines, including CMFH [31], DBRC [58], UDCMH [35], DJSRH [12], JDSH [36], DSAH [37], AGCH [26], DAEH [27], DRNPH [49], which are briefly described as follows:  
* CMFH: This method learns uniform binary feature vectors for different modalities through collective matrix factorization of latent factor models.
* DBRC: This approach proposes a deep binary reconstruction model to preserve inter-modal correlation.
* UDCMH: This method utilizes deep learning and matrix factorization with binary latent factor models for multi-modal data search.
* DJSRH: This approach integrates original neighborhood information from different modalities into a joint-semantics affinity matrix to extract latent intrinsic semantic relations.
* JDSH: This method introduces a distribution-based similarity decision and weighting scheme for generating a more discriminative hash code.
* DSAH: This approach explores similarity information across modalities and incorporates a semantic-alignment loss function to align features' similarities with those between hash codes.
* AGCH: This method utilizes GCNs to uncover semantic structures, coupled with a fusion module for correlating different modalities.
* DAEH: This approach attempts to train hash functions with discriminative similarity guidance and an adaptively-enhanced optimization strategy.
* DRNPH: This method implements unsupervised deep relative neighbor relationship preserving cross-modal hashing for achieving cross-modal retrieval in a common Hamming space.  
Except for CMFH, All other approaches use deep features to generate cross-modal hash codes.  
**Implementation Details.** As discussed above, the learning process is divided into two stages. In hash code learning stage, three hyperparameters \(\lambda_{1},\lambda_{2},\lambda_{3}\) are used to weight \(\mathcal{L}_{rec},\mathcal{L}_{mod},\mathcal{L}_{sim}\), respectively. In hash function learning stage, three other hyperparameter \(\beta_{1},\beta_{2},\beta_{3}\) are used to adjust the ratio between \(\mathcal{L}_{f_{1}},\mathcal{L}_{f_{2}},\mathcal{L}_{f_{3}}\). On the MIRFLICKR-25K dataset, we set \(\lambda_{1}=0.1,\lambda_{2}=1,\lambda_{3}=10,\beta_{1}=1,\beta_{2}=0.01,\beta_ {3}=1\). On NUS-WIDE dataset, we set \(\lambda_{1}=1,\lambda_{2}=1,\lambda_{3}=10,\beta_{1}=10,\beta_{2}=0.01,\beta_ {3}=10\). In the process of semantic similarity matrix construction, we set \(\alpha_{1}=0.6,\eta_{1}=0.8,\alpha_{2}=0.4,\eta_{2}=0.7\) on the MIRFLICKR-25K, and set \(\alpha_{1}=0.4,\eta_{1}=0.5,\alpha_{2}=0.7,\eta_{2}=0.6\) on the NUS-WIDE. In GSA sub-module, \(k\)NN algorithm is used to aggregate nodes in a certain neighborhood for each modality. We set \(k=40\) and \(k=60\) for MIRFLICKR-25K and NUS-WIDE, respectively. The optimization algorithm used is the Adam optimization algorithm [54]. For MIRFLICKR-25K, we set the learning rates for hash code learning and hash function learning to 0.001 and 0.0001, respectively, and for NUS-WIDE, both are set to 0.0001. The batch size is consistently set at 512. The number of iterations is defined as 60 for MIRFLICKR-25K and 100 for NUS-WIDE. It's worth noting that, under the same experimental setup, we directly utilize mAP@50 results provided in the original papers of the baseline methods.  
**Experimental Environment.** All the experiments are performed on a workstation with Intel(R) Core i9-12900K 3.9 GHz CPU, 128 GB RAM, 1 TB SSD storage, 2 TB HDD storage, and 1 NVIDIA GeForce RTX 3090 Ti GPU with ubuntu-22.04.1 operating system. All the techniques are implemented by Python 3.9 on PyTorch 2.0.1.