Multi-modal data uses data fusion technology and adopts identification process in the process of multi-modal biometric identification, which makes the authentication and identification process more accurate and safe.

Multimodal data is different from traditional data in the amount of information captured and the complexity of data. Modal data can capture a wide range of information, including visual and auditory cues, while traditional data is usually limited to a single modality.

Multi-modal refers to the use of different feature data in the same task to improve the recognition accuracy. Large model refers to the use of more parameters to improve the performance of the model, thus improving the recognition accuracy.

Modality refers to the interaction between people and the external environment (such as people, machines, objects, animals, etc.). ) Through their senses.

Although MAESTRO can learn the * * * representation from speech and text patterns through the modal matching algorithm under the framework of RNN-T, the algorithm can only be optimized on paired speech-text data. The goal of SpeechLM is to use text data to improve the learning of speech representation.

Now, it is more noteworthy to analyze multiple molecules in a single cell at the same time to establish a more comprehensive view of single-cell molecules. Generally, these methods combine the scRNA-seq data with other analytical methods. At present, there are four main strategies to obtain multimodal data from single cells: strictly speaking, this method is unimodal.