Abstract: An audio acquisition device positioning method is provided. In the method, a first image that includes an audio acquisition device is obtained. The audio acquisition device in the first image is identified. First coordinate data of the identified audio acquisition device in the first image is obtained. First displacement data is determined according to the first coordinate data and historical coordinate data of the audio acquisition device. First coordinates of the audio acquisition device are determined according to the first displacement data.

Abstract: An audio acquisition device positioning method is provided. In the method, a first image that includes an audio acquisition device is obtained. The audio acquisition device in the first image is identified. First coordinate data of the identified audio acquisition device in the first image is obtained. First displacement data is determined according to the first coordinate data and historical coordinate data of the audio acquisition device. First coordinates of the audio acquisition device are determined according to the first displacement data.

Abstract: A computer device segments a first sample region to obtain a candidate image region set that includes a plurality of candidate image regions. For each of the candidate image regions, the device obtains a first relationship degree corresponding to each candidate image region and obtains a second relationship degree corresponding to the candidate image region. The device obtains a relationship degree change value based on the first relationship degree and the second relationship degree. The device selects, from the plurality of candidate image regions, a first candidate image region as a target image region in accordance with a determination that the first candidate image region satisfies a condition in the relationship degree change value. The device performs model training based on the target image region to obtain a target detection model.

Abstract: An image classification method is provided to an electronic device. The method includes: receiving a target image and a reference image about the target image, the target image being a medical image; determining a first image feature of the target image and a second image feature of the reference image; fusing the first image feature and the second image feature to determine a to-be-classified image feature; and determining, by using the to-be-classified image feature, a probability that the target image belongs to a preset category.

Abstract: This application discloses a method for detecting a target in a video, performed by a computing device. The method includes applying a target detection operation to a first frame in the video, to determine a first target detection result of the target in the first frame; applying a target tracking operation to a second frame after the first frame in the video, to determine changes of the target between the first frame and the second frame; and determining a second target detection result of the target in the second frame according to the first target detection result and the changes of the target between the first frame and the second frame.

Abstract: A computer device segments a first sample region to obtain a candidate image region set that includes a plurality of candidate image regions, For each of the candidate image regions, the device obtains a first relationship degree corresponding to each candidate image region and obtains a second relationship degree corresponding to the candidate image region. The device obtains a relationship degree change value based on the first relationship degree and the second relationship degree. The device selects, from the plurality of candidate image regions, a first candidate image region as a target image region in accordance with a determination that the first candidate image region satisfies a condition in the relationship degree change value. The device performs model training based on the target image region to obtain a target detection model.

Abstract: This application discloses a method and apparatus for object detection in an image, a vehicle, and a robot. The method for object detection in an image is performed by a computing device. The method includes determining an image feature of an image; determining a correlation of pixels in the image based on the image feature; updating the image feature of the image based on the correlation to obtain an updated image feature; and determining an object detection result in the image according to the updated image feature.

Abstract: Embodiments of this application disclose an image processing method and apparatus, a device, and a storage medium. The method includes: inputting an original image to a decoder network according to an image transformation instruction, to obtain a first feature map of the original image; inputting the first feature map sequentially to a plurality of transformer networks, each transformer network corresponding to at least one piece of transformation requirement information associated with the original image, to obtain a second feature map, each of the transformer networks being configured to perform image transformation to a respective region of the first feature map; and inputting the second feature map to a reconstruction network, to obtain a target image, the reconstruction network being configured to reconstruct an inputted feature map into a two-dimensional image.

Abstract: Embodiments of this application disclose a method for training an image semantic segmentation model performed at a server, to locate all object regions in a raw image, thereby improving the segmentation quality of image semantic segmentation.

Abstract: A video target tracking method is provided to a computing device, the method includes: obtaining a partial detection map corresponding to a target image frame in a to-be-detected video; obtaining a relative motion saliency map corresponding to the target image frame; determining constraint information corresponding to the target image frame according to the partial detection map and the relative motion saliency map; adjusting a parameter of an image segmentation model by using the constraint information, to obtain an adjusted image segmentation model; and extracting a target object from the target image frame by using the adjusted image segmentation model.

Abstract: An image classification method is provided to an electronic device. The method includes: receiving a target image and a reference image about the target image, the target image being a medical image; determining a first image feature of the target image and a second image feature of the reference image; fusing the first image feature and the second image feature to determine a to-be-classified image feature; and determining, by using the to-be-classified image feature, a probability that the target image belongs to a preset category.

Abstract: A computer device segments a first sample region to obtain a candidate image region set that includes a plurality of candidate image regions, For each of the candidate image regions, the device obtains a first relationship degree corresponding to each candidate image region and obtains a second relationship degree corresponding to the candidate image region. The device obtains a relationship degree change value based on the first relationship degree and the second relationship degree. The device selects, from the plurality of candidate image regions, a first candidate image region as a target image region in accordance with a determination that the first candidate image region satisfies a condition in the relationship degree change value. The device performs model training based on the target image region to obtain a target detection model.

Abstract: Embodiments of this disclosure include an image processing method and apparatus. The method may include obtaining feature points corresponding to training images and feature center points of images in a second quantity of categories and obtaining a feature condition probability distribution that the feature points collide with corresponding feature center points. The method may further include performing network training to obtain target feature center points of the images in the second quantity of categories. The method may further include mapping the first quantity of feature points and the second quantity of target feature center points to a Hamming space, to obtain hash codes of the first quantity of training images and hash center points of the images in the second quantity of categories. The method may further include performing network training using the hash condition probability distribution and the ideal condition probability distribution, to obtain an image hash model.

Abstract: This application discloses a method and apparatus for object detection in an image, a vehicle, and a robot. The method for object detection in an image is performed by a computing device. The method includes determining an image feature of an image; determining a correlation of pixels in the image based on the image feature; updating the image feature of the image based on the correlation to obtain an updated image feature; and determining an object detection result in the image according to the updated image feature.

Abstract: This application discloses a method for detecting a target in a video, performed by a computing device. The method includes applying a target detection operation to a first frame in the video, to determine a first target detection result of the target in the first frame; applying a target tracking operation to a second frame after the first frame in the video, to determine changes of the target between the first frame and the second frame; and determining a second target detection result of the target in the second frame according to the first target detection result and the changes of the target between the first frame and the second frame.

Abstract: An audio acquisition device positioning method is provided. In the method, a first image that includes an audio acquisition device is obtained. The audio acquisition device in the first image is identified. First coordinate data of the identified audio acquisition device in the first image is obtained. First displacement data is determined according to the first coordinate data and historical coordinate data of the audio acquisition device. First coordinates of the audio acquisition device are determined according to the first displacement data.

Abstract: A depth information determining method for dual cameras is provided. A tth left eye matching similarity from a left eye image captured by a first camera of the dual cameras to a right eye image captured by a second camera of the dual cameras is obtained. A tth right eye matching similarity from the right eye image to the left eye image is obtained. The tth left eye matching similarity and a (t?1)th left eye attention map are processed with a neural network model, to obtain a tth left eye disparity map. The tth right eye matching similarity and a (t?1)th right eye attention map are processed with the neural network model, to obtain a tth right eye disparity map. First depth information is determined according to the tth left eye disparity map. Second depth information is determined according to the tth right eye disparity map.

Abstract: Embodiments of this application disclose an image processing method and apparatus, a device, and a storage medium. The method includes: inputting an original image to a decoder network according to an image transformation instruction, to obtain a first feature map of the original image; inputting the first feature map sequentially to a plurality of transformer networks, each transformer network corresponding to at least one piece of transformation requirement information associated with the original image, to obtain a second feature map, each of the transformer networks being configured to perform image transformation to a respective region of the first feature map; and inputting the second feature map to a reconstruction network, to obtain a target image, the reconstruction network being configured to reconstruct an inputted feature map into a two-dimensional image.

Abstract: Embodiments of this application disclose a method for training an image semantic segmentation model performed at a server, to locate all object regions in a raw image, thereby improving the segmentation quality of image semantic segmentation.

Abstract: A depth information determining method for dual cameras is provided. A tth left eye matching similarity from a left eye image captured by a first camera of the dual cameras to a right eye image captured by a second camera of the dual cameras is obtained. A tth right eye matching similarity from the right eye image to the left eye image is obtained. The tth left eye matching similarity and a (t?1)th left eye attention map are processed with a neural network model, to obtain a tth left eye disparity map. The tth right eye matching similarity and a (t?1)th right eye attention map are processed with the neural network model, to obtain a tth right eye disparity map. First depth information is determined according to the tth left eye disparity map. Second depth information is determined according to the tth right eye disparity map.