Abstract: A neckband earphone is provided, and a method, a system and a device for switching functions of the neckband earphone are provided. A key is provided on each of a left portion and a right portion of a neckband of the neckband earphone. The neckband is provided with a detector and a processor connected with the detector. The detector is configured to detect and determine a wearing orientation of the neckband earphone. The processor is configured to determine a function of the key on the left portion and a function of the key on the right portion based on the wearing orientation, to control key functions on a left side and a right side of a human body wearing the neckband earphone to remain unchanged for different wearing orientations.

Abstract: Disclosed herein is a lighting device with adjustable technical effects using few extraction electrodes to keep the cost low and the device simple, the device including: at least one filament group, wherein each filament group includes at least one filament, each filament includes at least one first light emitting unit and at least one second light emitting unit, which are connected in anti-parallel, and the first light emitting unit and the second light emitting unit have different light attributes from each other; and a light attribute adjusting unit, which is connected to both ends of each filament group to adjust a light attribute of light emitted from the lighting device, wherein the light attribute of the emitted light can be different from the light attribute of any light emitting unit.

Abstract: The application relates to a sensing device and a lighting device. The sensing device comprises a sensor provided with a glass window to transmit light and is configured to sense light incident upon the sensor; a circuit board, wherein one side of the circuit board is provided with the sensor; a Fresnel lens arranged above the sensor and configured to transmit light to the sensor; and a housing made from a flame-resistant material, wherein the housing comprises an accommodation space configured to accommodate the sensor and the circuit board, and the housing is provided with a center hole to expose the glass window. By adopting the technical solution, the sensor has flame-resistant performance.

Abstract: Disclosed are a pulp for Juncao spinning and a preparation method and use thereof. The method includes: placing a Juncao in a solution prepared from a lye, a catalyst, and an auxiliary agent and cooking to obtain a cooked solution, and then subjecting the cooked solution to pulping to obtain the pulp for Juncao spinning.

Abstract: This application discloses a method for identifying an intelligent surface device, a communication device, and an intelligent surface device, which relate to the field of wireless communication technologies. The method for identifying an intelligent surface device in embodiments of this application includes: sending, by a first communication device, a first signal; and obtaining, by the first communication device, identification information related to an intelligent surface device, where the identification information is information obtained by detecting an echo signal of the first signal by the first communication device, or information that is obtained by detecting an echo signal of the first signal by a second communication device and then is forwarded to the first communication device.

Abstract: A semiconductor device includes a substrate, a pair of semiconductor fins, a dummy fin structure, a gate structure, a plurality of source/drain structures, a crystalline hard mask layer, and an amorphous hard mask layer. The pair of semiconductor fins extend upwardly from the substrate. The dummy fin structure extends upwardly above the substrate and is laterally between the pair of semiconductor fins. The gate structure extends across the pair of semiconductor fins and the dummy fin structure. The source/drain structures are above the pair of semiconductor fins and on either side of the gate structure. The crystalline hard mask layer extends upwardly from the dummy fin and has an U-shaped cross section. The amorphous hard mask layer is in the first hard mask layer, wherein the amorphous hard mask layer having an U-shaped cross section conformal to the U-shaped cross section of the crystalline hard mask layer.

Abstract: The disclosure provides a method and a device for processing information, an electronic device, and a storage medium, belonging to a field of artificial intelligence including computer vision, deep learning, and natural language processing. In the method, the computing device recognizes multiple text items in the image. The computing device classifies multiple text items into a first set of name text items and a second set of content text items based on semantics of the text items. The computing device performs a matching operation between the first set and the second set based on a layout of the text items in the image, and determines matched name-content text items. The matched name-content text items include a name text item in the first set and a content text item matching the name text item and in the second set. The computing device outputs the matched name-content text items.

Abstract: A method and apparatus for processing an image, a device and a storage medium are provided. An implementation of the method includes: acquiring a template image, the template image including at least one region of interest; determining a first feature map corresponding to each region of interest in the template image; acquiring a target image; determining a second feature map of the target image; and determining at least one region of interest in the target image according to the first feature map and the second feature map.

Abstract: There is provided an image-based information extraction model, method, and apparatus, a device, and a storage medium, which relates to the field of artificial intelligence (AI) technologies, specifically to fields of deep learning, image processing, computer vision technologies, and is applicable to optical character recognition (OCR) and other scenarios. A specific implementation solution involves: acquiring a to-be-extracted first image and a category of to-be-extracted information; and inputting the first image and the category into a pre-trained information extraction model to perform information extraction on the first image to obtain text information corresponding to the category.

Abstract: A text recognition method includes: acquiring an image including text information, the text information including M characters, M being a positive integer greater than 1; performing text recognition on the image to acquire character information about the M characters; recognizing reading direction information about each character in accordance with the character information about the M characters, the reading direction information being used to indicate a next character corresponding to a current character in a semantic reading order; and ranking the M characters in accordance with the reading direction information about the M characters to acquire a text recognition result of the text information.

Abstract: A method for training an image recognition model includes: obtaining a training data set, in which the training data set includes first text images of each vertical category in a non-target scene and second text images of each vertical category in a target scene, and a type of text content involved in the first text images is the same as a type of text content involved in the second text image; training an initial recognition model by using the first text images, to obtain a basic recognition model; and modifying the basic recognition model by using the second text images, to obtain an image recognition model corresponding to the target scene.

Abstract: A pre-training method for a Vision and Scene Text Aggregation model includes: acquiring a sample image-text pair; extracting a sample scene text from a sample image; inputting a sample text into a text encoding network to obtain a sample text feature; inputting the sample image and an initial sample aggregation feature into a visual encoding subnetwork and inputting the initial sample aggregation feature and the sample scene text into a scene encoding subnetwork to obtain a global image feature of the sample image and a learned sample aggregation feature; and pre-training the Vision and Scene Text Aggregation model according to the sample text feature, the global image feature of the sample image, and the learned sample aggregation feature.

Abstract: Embodiments of the present disclosure provide a method for training a face fusion model and an electronic device. The method includes: performing a first face changing process on a user image and a template image to generate a reference template image; adjusting poses of facial features of the template image based on the reference template image to generate a first input image; performing a second face changing process on the template image to generate a second input image; inputting the first input image and the second input image into a generator of an initial face fusion model to generate a fused face area image; and inputting the fused image and the template image into a discriminator of the initial face fusion model to obtain a result, and performing backpropagation correction on the initial face fusion model based on the result to generate a face fusion model.

Abstract: Provided are a method for training a region of interest (ROI) detection model, a method for detecting an ROI, a device, and a medium. The specific implementation includes: performing feature extraction on a sample image to obtain a sample feature data; performing non-linear mapping on the sample feature data to obtain a first feature data and a second feature data; determining an inter-region difference data according to the second feature data and a third feature data of the first feature data in a region associated with a label ROI; and adjusting at least one of a to-be-trained feature extraction parameter and a to-be-trained feature enhancement parameter of the ROI detection model according to the inter-region difference data and the region associated with the label ROI.

Abstract: A method and an apparatus is provided for recognizing a document image, a storage medium and an electronic device, relates to the technical field of artificial intelligent recognition, particularly relates to the technical fields of deep learning and computer vision. The method includes that a document image to be recognized is transformed into an image feature map, where the document image at least includes at least one text box and text information including multiple characters; a first recognition content of the document image to be recognized is predicted based on the image feature map, the multiple characters and the text box; the document image to be recognized is recognized based on an optical character recognition algorithm to obtain a second recognition content; and the first recognition content is matched with the second recognition content to obtain a target recognition content.

Abstract: The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Abstract: A method for recognizing text includes: obtaining a first feature map of an image; for each target feature unit, performing a feature enhancement process on a plurality of feature values of the target feature unit respectively based on the plurality of feature values of the target feature unit, in which the target feature unit is a feature unit in the first feature map along a feature enhancement direction; and performing a text recognition process on the image based on the first feature map after the feature enhancement process.

Abstract: The present disclosure provides a character detection method and apparatus, a model training method and apparatus, a device and a storage medium. The specific implementation is: acquiring a training sample, where the training sample includes a sample image and a marked image, and the marked image is an image obtained by marking a text instance in the sample image; inputting the sample image into a character detection model, to obtain segmented images and image types of the segmented images output by the character detection model, where the image type indicates that the segmented image includes a text instance, or the segmented image does not include a text instance; and adjusting a parameter of the character detection model according to the segmented images, the image types of the segmented images and the marked image.

Abstract: A method for text recognition is disclosed. The method includes obtaining a whole-image scenario for an image to be processed and a text image in the image to be processed. The method further includes determining a first text recognition model corresponding to the whole-image scenario. The method further includes performing text recognition on the text image according to the first text recognition model to obtain text information.

Abstract: A method of processing a task, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence, in particular to fields of deep learning and computer vision, and may be applied to OCR optical character recognition and other scenarios. The method includes: parsing labeled data to be processed according to a task type identification, to obtain task labeled data, a tag information of the task labeled data is matched with the task type identification, and the task labeled data includes first task labeled data and second task labeled data; training a model using the first task labeled data, to obtain candidate models, the model is determined according to the task type identification; and determining a target model from the candidate models according to a performance evaluation result obtained by performing performance evaluation on the plurality of candidate models using the second task labeled data.