The innovation of multimodal semantic segmentation can be achieved through the following aspects:
Data: Using more abundant and diverse training data can improve the generalization ability of the model.
Model: Explore more advanced neural network structures, such as convolutional neural network, residual network, etc., to improve the accuracy of the model.
Algorithms: Experiment with new segmentation algorithms such as Adversarial Generative Networks, Generative Adversarial Networks, etc.
Fusion: Fusion of multimodal data to improve the accuracy of segmentation.
Application: Apply multimodal semantic segmentation technology to new fields, such as medical image analysis, natural language processing, etc., to expand its application range.