5results about How to "Guaranteed recall" patented technology

The application provides a sparse-to-dense visual positioning method and system based on feature Gaussian splash, and the method comprises the following steps: initializing a color-decoupled feature Gaussian field based on a training image set, optimizing the color-decoupled feature Gaussian field based on a query feature map set in combination with feature rendering and feature alignment loss cyclic optimization, and outputting a compact feature Gaussian scene model; screening a Gaussian landmark set in the compact feature Gaussian scene model by using a matching-oriented sampling strategy; training a scene-specific detector; extracting sparse local features of a query landmark heat map corresponding to a query image and performing sparse feature matching with the Gaussian landmark set to obtain an initial pose of a query perspective camera; based on 3D Gaussian splash, rendering a dense feature map and a depth map of the query perspective in the compact feature Gaussian scene model by using the initial pose of the query perspective camera, performing cluster-based proxy matching-based sparse-to-dense accelerated pose optimization, and obtaining accurate positioning of the query perspective camera.

The application relates to the technical field of data processing, in particular to an image clustering method, a computer device and a storage medium. The image clustering method provided by the application comprises the following steps: acquiring a plurality of images, and separating a first-class image with high image quality and a second-class image with low image quality from the images; acquiring a real-time library archive from a real-time library, performing real-time clustering on the first-class image based on the real-time library archive, and integrating the first-class image that has passed the real-time clustering into the real-time library archive, and adding the first-class image that has failed in the real-time clustering as a class cluster; acquiring the second-class image, the class cluster and the first-class image in the real-time library archive, and performing offline clustering on the second-class image and the first-class image; and adding the second-class image that has passed the offline clustering into the class cluster corresponding to the first-class image, or updating the second-class image that has passed the offline clustering into the real-time library archive corresponding to the first-class image. Through the above method, high-quality images can be quickly clustered, and low-quality images can be effectively recycled, so that the efficiency, real-time performance and integrity of the clustering are ensured.

This application discloses an information acquisition method, apparatus, device, and storage medium, belonging to the field of artificial intelligence technology. The method includes: acquiring the intent classification result of a natural language question, whereby the intent classification result indicates the intent type corresponding to the natural language question; determining a target retrieval method from multiple retrieval methods based on the intent classification result; wherein the multiple retrieval methods are used to acquire knowledge at different levels of expertise; and retrieving the answer to the natural language question from the knowledge base corresponding to the target retrieval method using the target retrieval method. This application ensures the professionalism of the answers by retrieving answers at different levels of expertise for natural language questions with different intent types, thereby improving the accuracy and flexibility of information acquisition. Furthermore, since this application is not limited to obtaining answers from a single knowledge base, it ensures the recall of information. This application can be applied to intelligent question answering in the medical field (such as medication and medical consultation).

This invention relates to the field of intelligent manufacturing and industrial quality management technology, and in particular to a management system for die-cast housings of new energy vehicles. The system includes a data acquisition module for periodically collecting production data, quality data, and production equipment data of the die-cast housings; a data analysis module for determining the defect type of the die-cast housings within a single cycle based on the average defect density and defect clustering index of the die-cast housings within that cycle; a traceability module for determining different traceability methods based on the defect type of the die-cast housings within a single cycle; and an adjustment module for adjusting the judgment parameters for the defect type of the die-cast housings within a single cycle based on the defect traceability accuracy. This invention, through automatic and accurate defect classification and differentiated traceability, quickly locates the root cause of problems, greatly shortens the problem response time, and reduces the generation of defective products.

This invention belongs to the field of computer vision and artificial intelligence technology, and relates to a multi-target tracking and segmentation method and system based on feature fusion and spatiotemporal memory. The multi-target tracking and segmentation method includes: acquiring the original state features and predicted bounding boxes of each target in each frame of the image, and constructing a spatiotemporal memory database containing a short-term buffer and a long-term storage area; for the first detected target in the multi-target video sequence, instantiating an independent long-term trajectory prototype for it, and establishing a corresponding index in the spatiotemporal memory database; performing hierarchical matching of the original state features and predicted bounding boxes of each target in the frame of the image based on the spatiotemporal memory database; updating the Kalman filter parameters and the spatiotemporal memory database based on the matching results; and continuing the tracking and segmentation of the next frame of the image using the updated Kalman filter and spatiotemporal memory database until each frame of the image has been traversed to obtain the multi-target tracking and segmentation result. This invention can effectively solve the problem of re-identification after long-term target occlusion.