4results about How to "Optimize network parameters" patented technology

This invention relates to the field of digital image security technology, and in particular provides a robust feature embedding image watermarking method based on noise-aware guidance. The method includes acquiring a carrier image dataset, constructing a robust feature embedding image watermarking network based on noise-aware guidance, the robust feature embedding image watermarking network including a watermark embedding architecture and a watermark extraction architecture; inputting the carrier image into the watermark embedding architecture to obtain a watermarked image; inputting the watermarked image into the watermark extraction architecture to obtain watermark information; optimizing network parameters, and simultaneously using a discriminator as an adversarial network, utilizing adversarial loss to ensure that the carrier image and the watermarked image are similar. This method improves robustness against attacks while generating high-quality watermarked images.

The application discloses a kind of underground image monitoring method and equipment based on unsupervised learning network, it is related to mineral exploitation underground image monitoring technical field, the method comprises: the monitoring model corresponding to target monitoring task is target monitoring model;Target monitoring task scene data set is obtained;Based on target monitoring task, unsupervised learning network and the loss function of unsupervised learning network are constructed;Using target monitoring task scene data set, loss function and target monitoring model, unsupervised learning network is trained;Using target monitoring model and the unsupervised learning network trained, executes target monitoring task.The application improves the accuracy of underground image monitoring by constructing and training unsupervised learning network.

This invention discloses a dynamically densely connected spatiotemporal feature decoupling network for recognizing cross-view gait, relating to the field of computer vision. It includes: an initial feature processing module, a dynamically dense spatiotemporal decoupling feature extraction module, and a feature enhancement processing module. The module uses dense spatiotemporal feature decoupling blocks and concatenation operations to achieve the sharing of shallow and deep network features, thereby addressing the problem of insufficient representation ability. Simultaneously, it employs an enhanced convolutional block attention mechanism to allow the network to focus on more important gait features. Finally, the feature enhancement processing module processes the five-dimensional feature mapping into multiple lateral features and performs batch standardization of the features to enhance representation ability and model generalization ability. This allows for the mining of correlations between shallow and deep features, alleviating the problem of insufficient information representation ability.

The application discloses a human pose reconstruction method based on a distance-guided double-branch network. The purpose is to solve the problems of the loss of joint information of slow movement or almost static, the lack of unified range prior between different representations, and the difficulty in effectively combining human motion saliency and structural integrity. The method comprises the following steps: acquiring a multi-channel millimeter wave radar echo signal; estimating human distance prior based on absolute median difference; constructing distance-guided point cloud features and distance-guided heat map features; and based on a double-branch network of point cloud and heat map, and using a cross-modal attention mechanism to fuse the two representations. The application improves the accuracy and robustness of human pose reconstruction of the millimeter wave radar, effectively reduces the loss of human information caused by filtering, and is suitable for scenes such as smart elderly care and smart home.