# A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective
## 3 Fine-Grained Deep Learning Methodologies
### Unified V-L Architecture  
This section introduces how we study the unified architecture, which is crucial for learning vision and language information. We summarize the vision language (V-L) architecture into two categories: the universal representation and unified generation model from recent references. The universal representation aims to learn a single embedding space whereby multiple modalities may be represented. A unified generation model is a form of cross-modal retrieval that utilizes a single model to build content representations in several modalities. Both approaches have their advantages and disadvantages, and the choice of approaches depends on the specific requirements. Firstly, we present an overview of the two architectures in this section. Subsequently, we provide a comprehensive evaluation of the advantages and disadvantages, highlighting their strengths and weaknesses. Universal representation. Universal representation is essential for effectively comparing similarity across different modalities in cross-modal retrieval. To achieve this goal, the DSCMR model proposed by [74] presents a generic representation space that enables the direct comparison of samples from multiple modalities. The framework employs a supervised cross-modal learning approach to establish connections between disparate modalities, successfully learning common sentences while retaining semantic distinctions and modal invariance. To discover cross-modal correlations, the final layer of the model contains two subnets with weight-sharing restrictions. Modal invariance losses are incorporated into the objective function to remove discrepancies, and a linear classifier categorizes the data in the common representation space. These features collectively make the DSCMR model a promising approach for cross-modal retrieval. The proposed method in SDML [77] defines a public space beforehand while simultaneously maximizing the smallest group gap. SDML is the first model to support an infinite number of modal inputs. To train a specific network for different modalities, the input is projected into a predefined subspace. This approach trains additional modalities without learning all modals simultaneously. UNITER aims to solve the problem of determining whether it learns a common vision language representation for all V-L tasks. Its large-scale pre-training process allows it to handle diverse downstream V-L tasks and joint multi-modal embedding. Besides the joint representation, the universal encoder has also been studied extensively. For example, Unicoder-VL develops a generic vision and language encoder. Unicoder-VL employs three types of pre-training tasks, including MLM, MOC, and VLM. The tasks collaborate to create context-aware representations of input tokens. It also tries to predict whether a picture and a text are related and performs other algorithms without jointly pre-training for image-text retrieval. It illustrates that transferring learning may also produce excellent results in cross-modal tasks. GPV [78] provides a general purpose and task-agnostic system. It receives visual characteristics and textual descriptions. Besides, it generates bounding boxes, confidences, and output information. Without affecting the network structure, the system may learn and carry out any task across a large domain. GPV comprises an optical encoder, a textual encoder, and a co-attention module. The CNN backbone and the DETR transformer encoder-decoder are used to create an object detector. It also refers to ViLBERT, which can encode cross-contextualize representations from visual and linguistic encoders. Because collecting and annotating task-specific data in all languages is unfeasible, there is a strong need for a framework to make universal models across languages. MBP [79] provides a multi-language and multi-modal pre-training paradigm that integrates them into a cohesive framework to acquire universal representations.