Abstract: The present disclosure provides a method and an apparatus for face recognition and a computer readable storage medium. The method includes: inputting a to-be-recognized blurry face image into a generator of a trained generative adversarial network to obtain a to-be-recognized clear face image; inputting the to-be-recognized clear face image to the feature extraction network to obtain a facial feature of the to-be-recognized clear face image; matching the facial feature of the to-be-recognized clear face image with each user facial feature in a preset facial feature database to determine the user facial feature best matching the to-be-recognized clear face image as a target user facial feature; and determining a user associated with the target user facial feature as a recognition result. Through this solution, the accuracy of the recognition of blurry faces can be improved.

Abstract: The present disclosure can provide a method, an apparatus and a system for displaying comment information, a client, and a server. The client can determine a comment copywriting tag of a user account first, and then in response to a comment releasing instruction from the user account, obtain target comment information corresponding to the comment copywriting tag from a comment database, and release the target comment information in a comment interface of the user account.

Abstract: A backlight face recognition method, a terminal device using the same, and a computer readable storage medium are provided. The method includes: performing a face detection on each original face image in an original face image sample set to obtain a face frame corresponding to the original face image; capturing the corresponding original face images from the original face image sample set, and obtaining a new face image containing background pixels corresponding to the captured original face images from the original face image sample set; preprocessing all the obtained new face images to obtain a backlight sample set and a normal lighting sample set; and training a convolutional neural network using the backlight sample set and the normal lighting sample set until the convolutional neural network reaches a preset stopping condition. The trained convolutional neural network will improve the accuracy of face recognition in complex background and strong light.

Abstract: This application relates to an image processing method and apparatus, a storage medium, and a computer device. The method includes obtaining acquired image frames; identifying, in each obtained image frame, a target area and a reference area that are obtained through image semantic segmentation; detecting, when a location relationship between the target area and the reference area in an obtained first image frame meets an action start condition and a location relationship between the target area and the reference area in an obtained second image frame meets an action end condition, an action to trigger adding an additional element, the second image frame being acquired after the first image frame; obtaining the additional element when the triggering action is detected; and adding the additional element to image frames acquired after the second image frame.

Abstract: This application discloses a method, an apparatus, and a storage medium for gesture movement recognition.

Abstract: A gesture recognition method includes: acquiring a target image containing a gesture to be recognized; inputting the target image to a gesture recognition model that has a first sub-model, a second sub-model, and a third sub-model, the first sub-model is to determine a gesture category and a gesture center point, the second sub-model is to determine an offset of the gesture center point, and the third sub-model is to determine a length and a width of a bounding box for the gesture to be recognized; acquiring an output result from the gesture recognition model, the output result includes the gesture category, the gesture center point, and the offset of the gesture center point, and the length and the width of the bounding box; and determining the gesture category and a position of the bounding box of the gesture to be recognized according to the output result.

Abstract: The present application discloses a control method for fast trapping and high-frequency mutual ejection of cold atom groups. The control method includes: arranging three groups of optical stops on three groups of light sources (splitters) in three-dimensional magneto-optical traps, to form a shaded regions; ejecting a cold atom group from the first three-dimensional magneto-optical trap along a movement trajectory to the second three-dimensional magneto-optical trap, where the movement trajectory passes through the shaded regions of the two three-dimensional magneto-optical traps; and, when it is determined that the cold atom group enters the shaded region of the first three-dimensional magneto-optical trap, trapping a next cold atom group by turning on three-dimensional cooling light and three-dimensional repumping light in the first three-dimensional magneto-optical trap.

Abstract: A wireless communication method and apparatus are provided for selecting quality-reporting sub-carriers based on sub-carrier quantity information received from a base station. The method comprises: acquiring information indicating a number of sub-carriers; measuring channel quality of each of a plurality of sub-carriers within a communication band from a received signal; selecting sub-carriers satisfying a condition related to measured channel quality bands from the plurality of sub-carriers, wherein quantity of the selected sub-carriers corresponds to the quantity of sub-carriers indicated by the acquired information; and transmitting information indicating channel quality of the selected sub-carriers.

Abstract: A wireless communication apparatus is capable of improving communication efficiency by reducing the amount of control information transmitted. A channel quality information extraction section extracts CQI's from a received signal. An allocation control section allocates subcarriers for every communication terminal apparatus and selects a modulation scheme in such a manner that required transmission rate is satisfied for each communication terminal apparatus based on required transmission rate information, etc. and CQI's for communication terminal apparatus of each user. A required subcarrier number determining section decides the number of subcarriers allocated to every communication terminal apparatus so as to satisfy the required transmission rate for each communication terminal apparatus. A required subcarrier number information generating section generates information for the number of subcarriers allocated to every communication terminal apparatus.

Abstract: The disclosure discloses an array substrate and a preparation method thereof, a display panel and a display device. The array substrate includes: a substrate, and a first metal layer, a metal oxide layer and a second metal layer which are sequentially stacked and isolated from each other on the substrate; the first metal layer includes a light shading metal, a first electrode, and an anti-static line; the metal oxide layer includes a first active layer; the second metal layer includes a gate line and a second electrode; the gate line is connected with the anti-static line through a first TFT, one of the first electrode and the second electrode forms the source and drain electrodes of the first TFT, and the other forms the gate electrode of the first TFT; and the source is electrically connected with the gate line, and the drain is electrically connected with the anti-static line.

Abstract: A target object detection model is provided. The target object detection model includes a YOLOv3-Tiny model. Through the target object detection model, low-level information in the YOLOv3-Tiny sub-model can be merged with high-level information therein, so as to fuse the low-level information and the high-level information. Since the low-level information can be further used, the comprehensiveness of target detection is effectively improved, and the detection effect of small targets is improved.

Abstract: A display substrate, a preparation method thereof, and a display apparatus are provide. The display substrate includes: a base substrate, an active layer disposed on the base substrate, a first gate insulating layer disposed on the active layer, a first conductive layer disposed on the first gate insulating layer, and a second conductive layer disposed on the first conductive layer and electrically connected with the first conductive layer; an orthographic projection of the first conductive layer on the base substrate does not overlap with an orthographic projection of the active layer on the base substrate; the second conductive layer includes gates; orthographic projections of the gates on the base substrate and the orthographic projection of the active layer on the base substrate have an overlap area; and the display substrate further includes: at least one insulating layer located between the first conductive layer and the gates.

Abstract: A thin film transistor, a display panel and a preparation method thereof and a display apparatus are provided. The thin film transistor includes: a substrate; a gate metal located on a side of the substrate; a gate insulating layer located on a side of the gate metal away from the substrate; an active layer located on a side of the gate insulating layer away from the substrate; a first metal oxide and a second metal oxide which are located on a side of the active layer away from the substrate and are arranged on a same layer; and a source metal and a drain metal which are located on sides of the first metal oxide and the second metal oxide away from the substrate and are arranged in a same layer.

Abstract: The present disclosure provides a photoresist composition, a pixel definition layer, a display substrate and a method for preparing the same, and a display device. The photoresist composition includes: 5 to 25 wt % of polymethacrylate; 1 to 15 wt % of a lyophobic compound; 1 to 5 wt % of a temperature sensitive polymer; 0.5 to 2 wt % of a photoinitiator; and 0.1 to 1 wt % of a monomer.

Abstract: A method for recognizing object nodes in an image includes: performing image detection on an original image, to determine a target image region that comprises a target image object, the original image being a two-dimensional image; performing image cropping on the original image according to the target image region, to obtain a subimage comprising the target image object; and performing node recognition on the subimage based on a preset node recognition model, to obtain image position information of each node of the target image object in the original image.

Abstract: A material delivery method and apparatus, an electronic device, and a storage medium. The method comprises: acquiring a material delivery model for a target product (101), wherein the material delivery model comprises a delivery route for the target product, and the number of deliveries made by respective logistics conveying devices in a unit of time on the delivery route; generating, according to the number of deliveries in the unit of time, a delivery instruction for each of the logistics conveying devices (102); and sending the delivery instructions to the corresponding logistics conveying devices (103).

Abstract: A mask wearing status alarming method, a mobile device, and a computer readable storage medium are provided. The method includes: performing a face detection on an image to determine face areas each including a target determined as a face; determining a mask wearing status of the target in each face area; confirming the mask wearing status of the target in each face area using a trained face confirmation model to remove the face areas comprising the target being mistakenly determined as the face and determining a face pose in each of the remaining face areas to remove the face areas with the face pose not meeting a preset condition, in response to determining the mask wearing status as a not-masked-well status or a unmasked status; and releasing an alert corresponding to the mask wearing status of the target in each of the remaining face areas.

Abstract: A dynamic gesture recognition method includes: performing detection on each frame of image of a video stream using a preset static gesture detection model to obtain a static gesture in each frame of image of the video stream; in response to detection of a change of the static gesture from a preset first gesture to a second gesture, suspending the static gesture detection model and activating a preset dynamic gesture detection model; and performing detection on multiple frames of images that are pre-stored in a storage medium using the dynamic gesture detection model to obtain a dynamic gesture recognition result.

Abstract: A method for detecting blurriness of a human face in an image includes: performing a face detection in a target image; when a human face is detected in the target image, cropping the human face from the target image to obtain a face image and inputting the face image to a first neural network model to perform preliminary detection on a blurriness of the human face in the face image to obtain a preliminary detection result; and when the preliminary detection result meets a deep detection condition, inputting the face image to a second neural network model to perform deep detection on the blurriness of the human face in the face image to obtain a deep detection result.

Abstract: An object detection model generation method as well as an electronic device and a computer readable storage medium using the same are provided. The method includes: during the iterative training of the to-be-trained object detection model, the detection accuracy of the iteration nodes of the object detection model is sequentially determined according to the node order, and the mis-detected negative samples of the object detection model at the iteration nodes with the detection accuracy less than or equal to a preset threshold are enhanced. Then the object detection model is trained at the iteration node based on the enhanced negative samples and a first amount of preset training samples. After the training at the iteration nodes are completed, it returns to the step of sequentially determining the detection accuracy of the iteration nodes of the object detection model until the training of the object detection model is completed.