Abstract: Embodiments of the present disclosure disclose a method and apparatus for detecting a body. A specific embodiment of the method includes: acquiring a to-be-detected image, the to-be-detected image including a body image; importing the to-be-detected image into a pre-established body key point detection model to obtain a body frame of a body corresponding to the body image, a position of a body key point, and a judgment result of whether the body key point belongs to the body in the to-be-detected image; and outputting the body frame of the body corresponding to the body image, the position of the body key point, and the judgment result of whether the body key point belongs to the body in the to-be-detected image.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for compressing an image. A specific embodiment of the method includes: acquiring a to-be-processed image; and performing feature extraction on the to-be-processed image by using a convolutional neural network corresponding to a trained image compression model to obtain a plurality of feature maps, a difference between the to-be-processed image and a reconstructed image obtained by reconstructing the plurality of feature maps through a deconvolution neural network corresponding to a trained image reconstruction model satisfying a preset condition. This embodiment achieves compression of the data size of an image while ensuring the restoration quality of the image.

Abstract: The present disclosure provides a method and apparatus for generating training data for human face recognition, a device and a computer storage medium, wherein the method comprises: inputting accessory-not-worn face images into a generative network, to obtain accessory-worn face images; using the accessory-worn face images as second training data for building the human face recognition model; wherein the generative network is a generative network in a generative adversarial network obtained by pre-training with first training data, the first training data including the accessory-not-worn face images and accessory-worn face images corresponding to a same user identifier. In the present disclosure, the accessory-worn face images obtained in a data augmentation manner greatly expand the amount of training data for building the human face recognition model, and thereby improve the recognition accuracy of the accessory-worn face images.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for compressing an image. A specific embodiment of the method includes: acquiring a to-be-processed image; and performing feature extraction on the to-be-processed image by using a convolutional neural network corresponding to a trained image compression model to obtain a plurality of feature maps, a difference between the to-be-processed image and a reconstructed image obtained by reconstructing the plurality of feature maps through a deconvolution neural network corresponding to a trained image reconstruction model satisfying a preset condition. This embodiment achieves compression of the data size of an image while ensuring the restoration quality of the image.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for detecting a body. A specific embodiment of the method includes: acquiring a to-be-detected image, the to-be-detected image including a body image; importing the to-be-detected image into a pre-established body key point detection model to obtain a body frame of a body corresponding to the body image, a position of a body key point, and a judgment result of whether the body key point belongs to the body in the to-be-detected image; and outputting the body frame of the body corresponding to the body image, the position of the body key point, and the judgment result of whether the body key point belongs to the body in the to-be-detected image.

Abstract: The present disclosure provides a method and apparatus for building a human face recognition model, a device and a computer storage medium, wherein the method comprises: regarding a known user's face images annotated with ages as training samples; using the training samples to train a deep neural network to obtain a human face recognition model, the human face recognition model being used to perform user identification for input face images. The present disclosure can solve the problem about reduction of the face recognition rate caused by age changes, and improve robustness of face recognition for ages.

Abstract: The present disclosure provides a method and apparatus for generating training data for human face recognition, a device and a computer storage medium, wherein the method comprises: inputting accessory-not-worn face images into a generative network, to obtain accessory-worn face images; using the accessory-worn face images as second training data for building the human face recognition model; wherein the generative network is a generative network in a generative adversarial network obtained by pre-training with first training data, the first training data including the accessory-not-worn face images and accessory-worn face images corresponding to a same user identifier. In the present disclosure, the accessory-worn face images obtained in a data augmentation manner greatly expand the amount of training data for building the human face recognition model, and thereby improve the recognition accuracy of the accessory-worn face images.