3results about How to "Effective characterization" patented technology

This application provides a product recommendation method, apparatus, device, medium, and program product, relating to the field of artificial intelligence. The method includes: determining the dynamic characteristics of a customer based on historical data of the customer's use of application software; determining the static characteristics of a customer based on the customer's attribute information; determining the static characteristics of a product based on the product's attribute information; determining the dynamic characteristics of a product based on the product's click and purchase records; pairing the customer and product based on their dynamic and static characteristics to obtain customer-product pairs; inputting the dynamic and static characteristics of the customer-product pairs into a pre-trained matching probability determination model to obtain the matching probability of the customer-product pairs; and recommending products to the customer based on the matching probability of each customer-product pair. This application accurately characterizes the features of customers and products through dynamic and static dual-dimensional information. Based on this, the matching results of customers and products are input into the model, resulting in more accurate and reliable matching probabilities.

A monocular image-based three-dimensional lane line detection method, device and medium, two-dimensional semantic features are extracted from a monocular lane image, and three-dimensional perception features are obtained by using a pre-trained deep model; two-dimensional semantic features and three-dimensional perception features are fused, an initial lane query is enhanced, and a query representation for three-dimensional lane line prediction is obtained; the query representation is iteratively decoded layer by layer, the sampling offset in the decoding process is dynamically adjusted based on the uncertainty parameters obtained by decoding, and the two-dimensional semantic sampling values and three-dimensional perception sampling values obtained by sampling in the decoding process are adaptively weighted and fused to obtain a three-dimensional lane line prediction result; the detection network is trained and constrained based on geometric structure constraints, and the final three-dimensional lane line detection result is output. The present application improves the spatial perception ability, prediction stability and lane structure rationality of monocular three-dimensional lane line detection in complex road scenes without relying on additional depth sensors and depth labeling information.

The application discloses a land survey quality monitoring system for remote sensing image intelligent segmentation and ground object identification, relates to the technical field of remote sensing, and comprises an image identification module, a label comparison module and a monitoring module. Specifically, remote sensing images of land to be monitored are acquired through a remote sensing sensor, image regions are divided through structural complexity analysis, and the category probability vectors of the pixel positions of each region in the formed non-uniform region set are analyzed and acquired to construct a label map of the whole remote sensing image. Pixel-by-pixel category comparison is performed on the current label map and a reference label map to form a change mask map. Structural change regions are identified according to the change mask map. Through time sequence analysis, it is determined whether the corresponding candidate label inconsistent regions under different time phases are still structural change regions to acquire a judgment result. Based on the judgment result, an electronic map is updated.