4results about How to "Improve annotation accuracy" patented technology

The invention discloses a code abstract generation method and system based on hierarchical context awareness, and belongs to the field of natural language processing and software engineering. The method comprises the following steps: generating hierarchical annotations for a code warehouse based on a function call graph, firstly extracting a call relationship in the graph and eliminating a ring structure, and then classifying leaf nodes and generating annotations; a topological sequence from bottom to top is adopted in the core step, the importance score of a child node is calculated for each father node, child node annotations, class function descriptions and code bodies of the child nodes are fused, and a large language model is input to generate annotations till the whole graph is covered. The method is suitable for an automatic document generation scene of a complex software project, and through a technical path combining function call graph analysis, child node importance measurement and class function description, the accuracy and semantic consistency of code annotation are improved; the method can be widely applied to the fields of software maintenance, code understanding, intelligent development tools, automatic document generation of enterprise-level code warehouses and the like.

The application relates to the technical field of intelligent driving, in particular to a sparse-labeled obstacle label enhancement method and device, equipment and a storage medium, which comprises the following steps: based on the 3D box parameter of a sparse-labeled key frame target, a 3D box parameter change curve of the target is fitted and obtained, and interpolation is carried out based on the 3D box parameter change curve to obtain a 3D interpolation box of the target; a true value 2D box of a sparse-labeled adjacent key frame target is inversely projected to a 3D space to obtain a key frame 3D inverse projection box, interpolation is carried out to obtain a 3D inverse projection interpolation box, and a 2D optimized interpolation box is obtained by projection with the width-height ratio of the 3D inverse projection interpolation box as a constraint; the 3D interpolation box is corrected based on the 2D optimized interpolation box to obtain a 3D optimized interpolation box. The technical problem that the prior art completely depends on a 3D point cloud detection model, and the detection and interpolation precision sharply decreases due to insufficient point clouds in a point cloud sparse scene such as a long-distance, low obstacle and serious occlusion can be solved.

This invention relates to the field of spatial transcriptomics technology and provides a few-sample spatial transcriptomics cell annotation system and method based on graph cue learning, comprising: a preprocessing module; a graph construction module, which constructs spatial adjacency relationships based on cell spatial coordinates and constrains or weights graph connectivity relationships by combining inter-cell transcriptomics expression similarity; a graph representation learning module, which learns discriminative cell embedding representations from the cell graph structure under unsupervised or weakly supervised conditions; a cue fine-tuning module, which injects the category information contained in a small number of labeled samples into a pre-trained graph representation model in the downstream cell type annotation task; and a cell type prediction module, which completes cell type annotation based on a small number of labeled samples. This invention can fully integrate gene expression information and multi-source spatial relationships under conditions of very few labeled samples to construct highly discriminative cell representations, achieving high-precision automatic annotation of large-scale spatial transcriptomics data.

The application discloses a cell type annotation method, device, equipment and medium, relates to the technical field of bioinformatics and medical image processing, and comprises the following steps: constructing a whole image feature matrix and a spatial neighborhood graph according to cell self characteristics and cell spatial positions of single cell samples of historical multiple tissue images; inputting the whole image feature matrix and the spatial neighborhood graph into a cell annotation model comprising a feature encoder, a spatial aggregator, a classification decoder and a prototype consistency constraint module; mapping the whole image feature matrix into an initial hidden vector by the encoder, determining attention coefficients of effective neighboring cells on the single cell samples based on the spatial neighborhood graph, weighting and summing the initial hidden vector according to the attention coefficients by the aggregator, outputting a predicted type probability distribution according to spatial perception characteristics by the decoder to generate a reconstructed feature; obtaining a prototype consistency loss value by the constraint module; updating and iterating the model based on the prototype consistency loss value to obtain a target cell annotation model; and performing cell type annotation by using the model.