Abstract: The embodiments of the present disclosure provide a method and apparatus for determining a wearing state of an eye-wearable device. The eye-wearable device is provided with an image acquisition device, and the image acquisition device is arranged so as to be opposite to the eyes of a user when the eye-wearable device is being worn. The method includes: acquiring an image captured by the image acquisition device; and determining the wearing state of the eye-wearable device according to the image.

Abstract: Embodiments of the disclosure provides a scheme for interpupillary distance measurement, in particular to a method, an apparatus, an equipment, a storage medium for interpupillary distance measurement. According to embodiments described herein, during the interpupillary distance measurement, the control unit moves the two cameras from their respective initial positions to their respective end positions, and during the moving process, the two cameras are controlled to capture the left-eye image and the right-eye image, respectively. Further, the control unit determines corresponding calibration parameters for obtaining the interpupillary distance, and obtains the interpupillary distance of the user based on the corresponding calibration parameters and the captured images.

Abstract: The present disclosure provides an image processing method and apparatus, a device, a medium and a product. The method includes: first acquiring an eye image and a feature extraction result of the eye image; then determining a predicted position of a visible region of an eyeball, a predicted position of an entire region of an iris and a predicted position of an entire region of a pupil based on the feature extraction result; and then determining, based on these predicted positions, a predicted position of a visible region of the iris and a predicted position of a visible region of the pupil.

Abstract: The present disclosure relates to a glasses diopter identification method and apparatus, an electronic device, and a storage medium, the method includes: acquiring an image to be processed; the image to be processed is obtained by performing image acquisition on a lens of target glasses under irradiation of a light source, and the image to be processed includes an optical feature generated when the light source emits light to the lens of the target glasses; and inputting input data including the image to be processed to a glasses diopter identification model to obtain a glasses diopter of the target glasses.

Abstract: Embodiments of the disclosure provides a method, an apparatus of line-of-sight detection, an electronic device and a storage medium. The method includes: obtaining a current image frame and a first reference image frame, where in the current image frame is an eye image collected in real time, and the first reference image frame is an eye image collected before the current image frame; obtaining an inter-frame variation amount of the current image frame relative to the first reference image frame; and deriving a target gaze direction based on the inter-frame variation amount, where the target gaze direction is a first gaze direction or a second gaze direction, the first gaze direction is a gaze direction pre-generated based on the first reference image frame, and the second gaze direction is a gaze direction generated in real time based on the current image frame.

Abstract: Disclosed are a nanobody targeting influenza A virus nucleoprotein and an application thereof, and a nucleic acid encoding the nanobody. The present disclosure obtains one nanobody targeting influenza A virus nucleoprotein through construction of a nanobody phage library and phage screening technology. The nanobody targeting influenza A virus nucleoprotein is a VHH antibody fragment having an amino acid sequence shown in SEQ ID NO:1-SEQ ID NO:2. The nanobody in the present disclosure can specifically bind to influenza A virus nucleoprotein, and can be applied to the detection and analysis of influenza A virus, and has broad application prospects in the fields of early warning of the epidemic or outbreak of influenza A virus, as well as the detection of influenza A virus in the environment or human body.

Abstract: Disclosed are a preparation method and application of a single-chain antibody fragment targeting SARS-CoV-2 nucleocapsid protein, aiming to provide a single-chain antibody fragment with high affinity and strong specificity. The present disclosure obtains three single-chain antibody fragments targeting SARS-CoV-2 nucleocapsid protein through construction of a nanobody phage library and phage screening technology. The single-chain antibody fragment consists of a heavy chain variable region, a 15aa connecting peptide, and a light chain variable region connected in sequence, and has an amino acid sequence shown in SEQ ID NO: 1-SEQ ID NO: 3. The single-chain antibody fragment in the present disclosure has excellent binding performance and specificity with the SARS-CoV-2 nucleocapsid protein, can be applied to a variety of immunoassay platforms of the SARS-CoV-2, and has broad application prospects in the field of SARS-CoV-2 detection.

Abstract: Disclosed in the present application is a method for processing complex data, which relates to the technical field of data processing. The method includes: generating a query instruction set by execution parameters of a target screening template through a data processing model; transmitting the query instruction set to a data processing engine for the data processing engine to obtain data from each connected target data source based on the query instruction set; and after the data is standardized through the data processing model, importing the data into a target collection template for analysis. A worker only needs to set the corresponding collection template and screening template according to a research scenario, and there is no need to develop a software according to the research scenario, which reduces preliminary preparation time and improves efficiency of data processing.

Abstract: Disclosed are a nanobody targeting influenza A virus nucleoprotein and an application thereof, and a nucleic acid encoding the nanobody. The present disclosure obtains one nanobody targeting influenza A virus nucleoprotein through construction of a nanobody phage library and phage screening technology. The nanobody targeting influenza A virus nucleoprotein is a VHH antibody fragment having an amino acid sequence shown in SEQ ID NO:1-SEQ ID NO:2. The nanobody in the present disclosure can specifically bind to influenza A virus nucleoprotein, and can be applied to the detection and analysis of influenza A virus, and has broad application prospects in the fields of early warning of the epidemic or outbreak of influenza A virus, as well as the detection of influenza A virus in the environment or human body.

Abstract: Disclosed are a preparation method and application of a nanobody targeting dengue virus NS1 protein. The nanobody targeting dengue virus NS1 protein is a VHH antibody, which is obtained by constructing a nanobody phage library through phage screening technology, and has an amino acid sequence shown in SEQ ID NO: 1. The nanobody in the present disclosure exhibits high affinity and specificity when being bound to the dengue virus NS1 protein, and has advantages of good stability and low cost compared with monoclonal antibodies. Therefore, the nanobody in the present disclosure can be produced on a large scale, and has bright application prospects in the field of dengue virus detection and analysis.

Abstract: Disclosed are a preparation method and application of a single-chain antibody fragment targeting SARS-COV-2 nucleocapsid protein, aiming to provide a single-chain antibody fragment with high affinity and strong specificity. The present disclosure obtains three single-chain antibody fragments targeting SARS-COV-2 nucleocapsid protein through construction of a nanobody phage library and phage screening technology. The single-chain antibody fragment consists of a heavy chain variable region, a 15aa connecting peptide, and a light chain variable region connected in sequence, and has an amino acid sequence shown in SEQ ID NO: 1-SEQ ID NO: 3. The single-chain antibody fragment in the present disclosure has excellent binding performance and specificity with the SARS-COV-2 nucleocapsid protein, can be applied to a variety of immunoassay platforms of the SARS-COV-2, and has broad application prospects in the field of SARS-COV-2 detection.

Abstract: Disclosed are a preparation method and application of a nanobody targeting dengue virus NS1 protein. The nanobody targeting dengue virus NS1 protein is a VHH antibody, which is obtained by constructing a nanobody phage library through phage screening technology, and has an amino acid sequence shown in SEQ ID NO: 1. The nanobody in the present disclosure exhibits high affinity and specificity when being bound to the dengue virus NS1 protein, and has advantages of good stability and low cost compared with monoclonal antibodies. Therefore, the nanobody in the present disclosure can be produced on a large scale, and has bright application prospects in the field of dengue virus detection and analysis.

Abstract: A method for detecting a human behavior includes: obtaining an image to be detected; obtaining a plurality of key points and a plurality of pieces of position information respectively corresponding to the plurality of key points by key-point recognition on the image to be detected; grouping the plurality of key points based on the plurality of pieces of position information to obtain a plurality of key-point groups, the plurality of key-point groups at least including a part of the plurality of key points; and determining a target human behavior based on key points in the plurality of key-point groups.

Abstract: A method of training a target object detection model includes: extracting a plurality of feature maps of a sample image according to a training parameter, fusing the plurality of feature maps to obtain at least one fused feature map, and obtaining an information of a target object based on the at least one fused feature map, by using the target object detection model; determining a loss of the target object detection model based on the information of the target object and a tag information of the sample image, and adjusting the training parameter according to the loss of the target object detection model. A method of detecting a target object and an apparatus are also provided.

Abstract: Provided are a method and apparatus for evaluating image relative definition, a device and a medium, relating to technologies such as computer vision, deep learning and intelligent medical. A specific implementation solution is: extracting a multi-scale feature of each image in an image set, where the multi-scale feature is used for representing definition features of objects having different sizes in an image; and scoring relative definition of each image in the image set according to the multi-scale feature by using a relative definition scoring model pre-trained, where the purpose for training the relative definition scoring model is to learn a feature related to image definition in the multi-scale feature.

Abstract: Provided are an image processing method and apparatus, a device, and a storage medium, relating to the technical field of image processing, in particular to the artificial intelligence fields such as computer vision and deep learning. The specific implementation scheme is as follows: inputting a to-be-processed image into an encoding network to obtain a basic image feature, wherein the encoding network includes at least two cascaded overlapping encoding sub-networks which perform encoding and fusion processing on input data at at least two resolutions; and inputting the basic image feature into a decoding network to obtain a target image feature for pixel point classification, wherein the decoding network includes at least one cascaded overlapping decoding sub-network to perform decoding and fusion processing on input data at at least two resolutions respectively.

Abstract: Embodiments of the present disclosure provide a method and apparatus for processing an image, and relates to the field of computer vision technology. The method may include: acquiring a value to be processed, where the value to be processed is associated with an image to be processed; and processing the value to be processed by using a quality scoring model to generate a score of the image to be processed in a target scoring domain, where the score of the image to be processed in the target scoring domain is related to an image quality of the image to be processed.

Abstract: An object area measurement method and an apparatus are provided, relating to the computer vision and deep learning technology. The method includes acquiring an original image with a spatial resolution, the original image including a target object; acquiring an object identification model including at least two sets of classification models; generating one or more original image blocks based on the original image; performing operations on each original image block: scaling each original image block at at least two scaling levels to obtain scaled image blocks with at least two sizes, the scaled image blocks respectively corresponding to the at least two sets of classification models, and inputting the scaled image blocks into the object identification model to obtain an identification result of the target object; and determining an area of the target object based on the respective identification results of the one or more original image blocks and the spatial resolution.

Abstract: A method for recognizing a category of an image includes: acquiring a spectral image; training an image recognition model based on the spectral image, in which the image recognition model acquires a spectral semantic feature of each pixel, a minimum distance between each pixel and each category, and a spectral distance between a first spectrum of each pixel and a second spectrum of each category; splices them; and performs classification and recognition based on the spliced feature to output a recognition probability of each pixel under each category; determining a loss function of the image recognition model, adjusting the image recognition model based on the loss function, and returning to training the adjusted image recognition model based on the spectral image until training ends; recognizing a maximum recognition probability, output from a target image recognition model, and using a category corresponding to the maximum recognition probability as a target category.

Abstract: A training method for a human body attribute detection model includes: acquiring positive sample sub-images and negative sample sub-images respectively corresponding to a plurality of human body attribute categories; determining a plurality of first annotation attributes respectively corresponding to the plurality of positive sample sub-images; and a plurality of second annotation attributes respectively corresponding to the plurality of negative sample sub-images; and training an artificial intelligence model according to the plurality of positive sample sub-images, the plurality of negative sample sub-images, the plurality of first annotation attributes and the plurality of second annotation attributes to obtain the human body attribute detection model, so that the human body attribute detection model obtained by training can effectively model fine-grained attributes of the human body.