A Review of Research Methods for Cross-Modal Retrieval
Cross modal retrieval is a key field in multimodal learning,whose main goal is to find semantic relationships between different mo-dalities,so that it can retrieve samples with similar semantic features between different modalities.With the development of deep neural net-works,cross modal retrieval has attracted the attention of many scholars.The consistency comparison of input-output queries remains a chal-lenge due to their different modalities.To this end,first introduce the relevant concepts of cross modal retrieval,summarize the commonly used methods of cross modal retrieval based on real value representation and binary representation,and then focus on the application of deep learning models in cross modal retrieval,the main datasets and evaluation indicators of cross modal retrieval.Finally,propose the future devel-opment direction and existing main difficulties and challenges in this field,in order to provide reference and guidance for researchers in cross modal retrieval.
cross-modal retrievaldeep learningreal value representationbinary representation
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