Cross-Modal Generalization is an important research direction in the field of artificial intelligence, which involves how to transfer knowledge learned in one modality to another modality. The latest research progress includes methods such as multimodal unified expression, dual cross-modal information decoupling, multimodal EMA, meta-learning and alignment. These technologies are widely used in many fields such as intelligent medical care, multimodal interaction, and intelligent search. The main technical methods include dual encoders, fusion encoders, unified backbone networks, cross-modal instruction fine-tuning, and distributed agent systems. With the deepening of research, cross-modal generalization technology will continue to expand, bringing new opportunities and challenges to the development of intelligent systems.
What is Cross-Modal Generalization?
Cross-Modal Generalization refers to the use of knowledge learned in one or more specific modalities to improve the performance of the system in new and unseen modalities. Applicable to multimodal learning tasks, the model needs to process and understand different types of data, such as text, images, sounds, etc. The key to cross-modal generalization is how to effectively transfer the knowledge learned in some modalities to other modalities even if these modalities may be completely different in expression.
How cross-modal generalization works?
The working principle of cross-modal generalization can be summarized as follows: learning to extract a unified discrete representation from paired multimodal data in the pre-training stage, so that in downstream tasks, even if only one modality is annotated, the model can achieve zero-shot generalization capabilities for other unseen modalities. By pre-training a large amount of paired data, the unified expression of information from different modalities is achieved. It involves alignment at the coarse-grained level, or alignment at the fine-grained level based on the premise that information from different modalities can correspond one-to-one. Let different modalities serve as supervisory signals for each other's modalities, and map information from different modalities with the same semantics together. Using the teacher-student mechanism, different modalities are brought close to each other in the discrete space, and finally different modal variables with the same semantics are converged together. Based on the known sequence information of the current modality, predict the future information in the other modality, maximize the fine-grained mutual information between different modalities, gradually extract semantic information and approach each other.
Through these methods, cross-modal generalization can achieve rapid learning and generalization on new modalities, and perform well even when there are only a small number (1-10) of labeled samples in the target modality, especially in low-resource modalities, such as spoken language in rare languages.
Main applications of cross-modal generalization
Medical image analysis: In the medical field, cross-modal generalization technology can integrate medical images (such as X-rays, CT, MRI) with patients' clinical text information (such as medical records, diagnostic reports).
Intelligent transportation system: In intelligent transportation systems, cross-modal generalization technology can combine image and sound information for traffic scene recognition.
Multimedia retrieval: In the field of multimedia retrieval, cross-modal generalization technology can achieve cross-modal retrieval between multimedia data such as images, text and audio. Users can retrieve related images or videos by entering text descriptions, or find related text information by uploading images.
Antonomous driving: Autonomous driving systems need to process data from multiple sensors, such as cameras, radars, and lidars. Cross-modal generalization technology can fuse these different modal data to improve the vehicle's perception of the environment and the accuracy of decision-making.
Sentiment analysis: In the field of sentiment analysis, cross-modal generalization technology can combine multiple information such as text, voice, and expressions to more accurately understand the user's emotional state.
Speech recognition: In the field of speech recognition, cross-modal generalization technology can combine voice signals and text information to improve the accuracy of the recognition system
Natural language processing: In the field of natural language processing, cross-modal generalization technology can fuse text information with information from other modalities such as images and audio.
In image annotation tasks, the system can generate descriptive text based on the image content, or generate corresponding images based on the text description.
Challenges of cross-modal generalization
Alinment of multimodal data: A core problem in multimodal learning is alignment, which refers to identifying and associating data elements from different modalities. For example, in video analysis, alignment may involve matching a specific image in a video frame with the corresponding audio signal or text description. The alignment problem is challenging because it may rely on long-term dependencies in the data, there may be ambiguity in the segmentation of data of different modalities, and the correspondence between different modalities may be one-to-one, many-to-many, or even no clear correspondence.
Implementation of cross-modal unified expression: The key to cross-modal generalization is to achieve multi-modal unified expression through pre-training of a large amount of paired data. However, information of different modalities is not completely aligned, and directly using the previous method will cause multi-modal information that does not belong to the same semantics to be incorrectly mapped together. Therefore, how to achieve unified expression of multi-modal sequences at a fine-grained level is a technical difficulty.
Efficiency of self-supervised learning mechanism: Self-supervised learning is the core method of multi-modal pre-training model. How to design a unified and fine-grained modeling target that is more suitable for multi-modal data, and how to combine the perception-decision-integrated modeling method of reinforcement learning are the keys to improving the efficiency of self-supervised learning.
Data scarcity problem: In some fields, there is not enough labeled data to train deep learning models, which limits the training and generalization capabilities of the models. Transfer learning and domain adaptation are key means to solve this problem. How to effectively transfer the knowledge of the model in one field to a different but related field remains a challenge.
Generalization ability of the model: The current multimodal pre-trained model has limited generalization ability on new modalities. For example, existing models are difficult to process other modal inputs besides images and texts, and most existing models can only output text, making it difficult to simultaneously generate multi-modal information such as images and texts.
Computational cost: Large-scale pre-trained models rely on a large amount of training data and computing resources, which poses insurmountable obstacles to the development and deployment of models. How to reduce the computational cost of pre-training large models, including the amount of training data and the amount of model parameters, has important research and application value.
Development prospects of cross-modal generalization
As an important technology in the field of artificial intelligence, cross-modal generalization has broad development prospects. It will further integrate multiple modal information processing capabilities, including text, voice, images, videos, etc., and achieve deeper understanding and generation capabilities through innovative model architectures and pre-training strategies. With the development of technology, cross-modal generalization will not only be limited to the perception level, but will develop towards higher-level cognitive capabilities, including cross-modal semantic understanding and reasoning, as well as multimodal instruction fine-tuning, and enhancing the multimodal thinking chain of the model and other cognitive capabilities. Cross-modal generalization technology will be combined with distributed intelligent agent systems to achieve continuous learning and evolution through interaction with the external environment, and build an intelligent system that can adapt and optimize itself. In order to comprehensively evaluate the performance of large cross-modal language models, more evaluation standards covering a wide range of scenarios, with strong dynamics and consistency will be established. As the application of cross-modal generalization technology becomes more and more extensive, security and controllability will also become the focus of research to ensure that the development of technology will not bring potential risks and negative effects. Stronger autonomous controllability and modeling capabilities will become the core task of future research, especially in the context of global scientific and technological competition. The improvement of this capability will be of great significance to the country's scientific and technological development. In summary, cross-modal generalization technology is developing towards deeper multimodal fusion, higher-level cognitive capabilities, wider application scenarios, more comprehensive evaluation, and safe and controllable directions, indicating that artificial intelligence technology will achieve richer and deeper cross-modal interaction and understanding in the future.
