4results about How to "Avoid Manual Labeling" patented technology

This application discloses an automatic image annotation method, apparatus, device, and storage medium. The method includes: annotating an original screenshot and saving the annotation information and image resolution of the original screenshot, the annotation information including annotation coordinate positions; recognizing the text information annotated in the original screenshot, the text information including text content and text coordinate positions; obtaining the image resolution of a new screenshot and recognizing the text content of the new screenshot to obtain the text information of the new screenshot; calculating a scaling ratio based on the image resolutions of the original and new screenshots, and obtaining the number of occurrences of the text content corresponding to the annotation coordinate positions in the original screenshot in the new screenshot by comparing the text content in the original screenshot with the text content in the new screenshot; obtaining the annotation information of the new screenshot based on the number of occurrences and the scaling ratio, and annotating the new screenshot based on the annotation information of the new screenshot. This improves the technical problem of low annotation efficiency in existing technologies that rely on manual image annotation.

This application relates to a method, apparatus, device, medium, and program product for generating speech training data. The method includes: acquiring initial training data, the initial training data comprising at least one audio-text pair; processing the audio and text in each audio-text pair to obtain first timestamp information corresponding to each word in the audio-text pair; based on the audio in each audio-text pair, obtaining each sub-language event and second timestamp information corresponding to the sub-language event; based on the first timestamp information corresponding to each word and the second timestamp information corresponding to the sub-language event, generating text insertion positions corresponding to each sub-language event; and based on the text insertion positions corresponding to each sub-language event and the initial training data, obtaining target training data. This method can reduce costs.

According to the automatic grain sampling point generation method and device based on computer vision, automatic identification and three-dimensional positioning of a vehicle-mounted grain surface are achieved through a double-branch deep neural network fusing stereo matching and semantic segmentation, sampling points are generated randomly according to the automatic identification and the three-dimensional positioning, the sampling points are transmitted to sampling equipment, and full automation of grain sampling in and out of a warehouse is achieved. The method comprises the following steps: firstly, calibrating a binocular camera to obtain internal and external parameters, collecting a sampling vehicle image, then training a model by adopting a method of combining domain adaptive learning and semi-supervised learning, then obtaining a vehicle-mounted grain surface and three-dimensional coordinates thereof by utilizing the trained model, and randomly sampling; and mapping the coordinates of the sampling points from a camera coordinate system to a sampler coordinate system, and sending the coordinates to sampling equipment to finally complete automatic sampling. According to the device, deep learning and computer vision technologies are combined, automatic generation of grain in-out warehouse sampling points is achieved, grain depot management automation and sampling efficiency can be improved, and grain depot management informatization and intelligent technology development is further promoted.

This invention discloses an image data annotation method based on artificial intelligence, relating to the fields of computer vision and machine learning. It includes the following steps: S1 Model initialization step; S2 Intelligent pre-annotation and selection step; S3 Human-machine collaborative correction step; S4 Model iterative update step; S5 Closed-loop feedback step. This invention significantly improves efficiency: intelligent pre-annotation avoids manual annotation from scratch, and utilizes an active learning strategy to maximize the value of human intervention, increasing overall annotation efficiency by more than 50%. Dynamic quality improvement: the closed-loop iteration mechanism allows the model to continuously learn from human feedback, and the pre-annotation quality continuously improves with each iteration, forming a self-reinforcing virtuous cycle.