WiMi to Develop A Multimodal Information Fusion Detection Algorithm Based on GANs

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[{"type":"text","content":"BEIJING, June 12, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (\"WiMi\" or the \"Company\"), a leading global Hologram Augmented Reality (\"AR\") Technology provider, today announced that it is developing a multimodal information fusion detection algorithm based on generative adversarial networks(GANs). The multimodal information fusion detection algorithm is a method to improve detection accuracy and robustness by fusing data from different sensors or modalities using a GAN. It is implemented by training two neural networks, a generator and a discriminator, where the generator is responsible for generating false data samples, and the discriminator is responsible for distinguishing between accurate and inaccurate data. The two networks compete with each other for learning until the generator can produce sufficiently realistic data, and the discriminator cannot differentiate between true and false.\nIn multimodal information fusion detection, data from different sensors or modalities, such as image, sound, and text, can be fused and processed to obtain more comprehensive and accurate detection results. The generator uses local detail features and global semantic features to extract source image details and semantic information. Perceptual loss is added to the discriminator to make the data distribution of the fused image consistent with the source image, which improves the accuracy of the fused image. The fused features enter the interest pool network for coarse classification, the generated candidate frames are mapped to the feature map, and finally, the fully connected layer completes the target classification and localization.\nGANs have inherent advantages in image generation, allowing unsupervised fitting and approximation of accurate data distributions. Using generators and discriminators for adversarial purposes allows fused images to retain richer information, and the end-to-end network structure no longer requires the manual design of fusion rules.\nThe technical process of the GANs-based multimodal information fusion detection algorithm studied by WiMi includes data preprocessing, GANs model training, model testing, result evaluation, and optimization and improvement. Data from different sensors or modalities, such as image, sound, and text, are fused for fusion processing, improving target detection ac...

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