Abstract: A deep learning-based video editing method can allow for automated editing of a video, reducing or eliminating user input, saving time and labor investments, and thereby improving video editing efficiency. Attribute recognition is performed on an object in a target video using a deep learning model. A target object is selected that satisfies an editing requirement of the target video. A plurality of groups of pictures associated with the target object from the target video are obtained using editing. An edited video corresponding to the target video is generated using the plurality of groups of pictures.

Abstract: A product defect detection method and apparatus, an electronic device, and a storage medium are provided. A method includes: acquiring a multi-channel image of a target product; inputting the multi-channel image to a defect detection model, wherein the defect detection model includes a plurality of convolutional branches, a merging module and a convolutional headbranch; performing feature extraction on each channel in the multi-channel image by using the plurality of convolutional branches, to obtain a plurality of first characteristic information; merging the plurality of first characteristic information by using the merging module, to obtain second characteristic information; performing feature extraction on the second characteristic information by using the convolutional headbranch, to obtain third characteristic information to be output by the defect detection model; and determining defect information of the target product based on the third characteristic information.

Abstract: A deep learning-based video editing method can allow for automated editing of a video, reducing or eliminating user input, saving time and labor investments, and thereby improving video editing efficiency. Attribute recognition is performed on an object in a target video using a deep learning model. A target object is selected that satisfies an editing requirement of the target video. A plurality of groups of pictures associated with the target object from the target video are obtained using editing. An edited video corresponding to the target video is generated using the plurality of groups of pictures.

Abstract: A method and an apparatus for obtaining information are provided. The method may include: obtaining at least one image feature from a to-be-inspected image, where the to-be-inspected image includes an image of a to-be-inspected item, and the image feature is used to represent surface feature information of the to-be-inspected item; and importing the to-be-inspected image and the at least one image feature into a pre-trained defect detection model to obtain defect information corresponding to the to-be-inspected item, where the defect detection model is obtained by training using a sample image, a sample image feature and sample defect information, and configured to represent a corresponding relationship between the to-be-inspected image and the at least one image feature.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method can include: acquiring an image of a to-be-inspected object; segmenting the image into at least one subimage; for a subimage in the at least one subimage, inputting the subimage into a pre-trained defect classification model to obtain a defect category corresponding to the subimage; and outputting defect information of the object based on a defect category corresponding to each subimage.

Abstract: A product defect detection method and apparatus, an electronic device, and a storage medium are provided. A method includes: acquiring a multi-channel image of a target product; inputting the multi-channel image to a defect detection model, wherein the defect detection model includes a plurality of convolutional branches, a merging module and a convolutional headbranch; performing feature extraction on each channel in the multi-channel image by using the plurality of convolutional branches, to obtain a plurality of first characteristic information; merging the plurality of first characteristic information by using the merging module, to obtain second characteristic information; performing feature extraction on the second characteristic information by using the convolutional headbranch, to obtain third characteristic information to be output by the defect detection model; and determining defect information of the target product based on the third characteristic information.

Abstract: Provided are a method and an apparatus for component fault detection based on an image, and a specific implementation is: when it is determined that an image shot by an image pickup apparatus for a component to be tested with a first shooting parameter does not meet a preset condition, adjusting the first shooting parameter to a second shooting parameter; controlling the image pickup apparatus to shoot for the component to be tested with the second shooting parameter to obtain a first image that meets the preset condition; and performing fault detection on the component to be tested according to the first image. The image pickup apparatus can be adjusted in real time, so that the image can be used for fault detection only when meeting the preset condition, thereby the image is kept stable, and the accuracy rate of component fault identification based on an image is improved.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method can include: acquiring an image of a to-be-inspected object; segmenting the image into at least one subimage; for a subimage in the at least one subimage, inputting the subimage into a pre-trained defect classification model to obtain a defect category corresponding to the subimage; and outputting defect information of the object based on a defect category corresponding to each subimage.

Abstract: Techniques are described to exchange location information between a location-aware client device and friend devices via a server that is configured to communicate with one or more friend finding services. The server may reformat the location information for communication between the client device and the friend finding services when the location information is provided in a format that is not supported by one or more of the client device, the friend device, or the one or more friend finding services.