Research on Multimodal Generative Adversarial Networks in the Framework of Deep Learning
DOI:
https://doi.org/10.54097/7gfu24akKeywords:
Image Recognition, Cross-modal, Generate Adversarial Network, Triplet LossAbstract
This project aims to align facial and vocal characteristics within a closely related common space through the construction of multi-modal generative adversarial networks (GANs). The project proposes a multi-modal approach grounded in visual perception, utilizing the Graph Cut algorithm to align feature components with the image features of each corresponding local context, thereby achieving adaptability in multi-modal information. To enhance the speed and accuracy of the modeling process, a regional attention strategy is integrated. Experimental results demonstrate that the proposed algorithm enhances the accuracy of image recognition tasks.
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