# A Cross-Modal Hash Retrieval Method with Fused Triples
## 3 Description of Existing Cross-Modal Retrieval Methods  
CCA-ITQ [18] is a typical correlation analysis iterative quantification methodology proposed by [PERSON] et al. in 2012. It is an unsupervised cross-modal hash retrieval method that provides multivariate statistics by evaluating the similarity of two sets of variables.  
CMFH [19] is the collective matrix factorization hashing method proposed by [PERSON] et al. in 2014. CMFH learns unified hash codes using collective matrix factorization with latent factor model from different modalities of one instance, which can not only support cross-view search but also increase the search accuracy by merging multiple view information sources. It belongs to the unsupervised cross-modal hashing retrieval techniques.  
SCM [7], a semantic correlation maximization method proposed by [PERSON] et al. in 2014, uses label information to construct a matrix of similarity between different modalities, a classical approach in supervised cross-modal hash retrieval.  
SePH [23], a semantic preservation hashing method proposed by [PERSON] et al. in 2015, uses label information to construct a supervised matrix and learns the hash function between the supervised matrix and the corresponding hash code using KL scatter to maintain the similarity between the learned hash code and the original data. This method is the standard for supervised cross-modal hash retrieval.  
DVSH [26] was proposed in 2016 by [PERSON] et al., and consists of a hybrid deep architecture that constitutes a visual-semantic fusion network for learning joint embedding space of images and text sentences and two modality-specific hashing networks for learning hash functions to generate compact binary codes. It is the leading approach in the supervised cross-modal hashing retrieval field.  
DCMH [15], a deep cross-modal hashing approach proposed by [PERSON] et al. in 2017, employs deep learning methods to combine feature extraction and hash learning into an end-to-end network architecture. It is the leading approach in the supervised cross-modal hashing retrieval field, and its emergence paved the way for deep cross-modal hashing retrieval.  
[PERSON] et al. proposed TDH [27] in 2018, which involves a triplet-based deep hashing network for cross-modal retrieval, utilizing the triple labels, which describe the relative relationships among three instances as supervision to capture more general semantic correlations between cross-modal models, and graph regularization is introduced into the TDH method to preserve the original semantic similarity between hash codes in Hamming space. It is the leading approach in the supervised cross-modal hashing retrieval field.  
DLFH [2], proposed in 2019 by [PERSON] et al., introduced a discrete latent factor cross-modal hashing approach that can directly learn binary hash codes belonging to the discrete method. This method is effective in the field of supervised cross-modal hash retrieval.  
DMSFH [25] proposed in 2022 by [PERSON] et al., consists of deep multi-semantic fusion-based cross-modal hashing, which uses a multi-label semantic fusion method to improve cross-modal consistent semantic discrimination learning. Moreover, a graph regularization method combines inter-modal and intra-modal pairwise loss to preserve the nearest neighbor relationship between data in the Hamming subspace. It is the leading approach in the supervised cross-modal hashing retrieval field.