Abstract: The provided are a face pose estimation method and apparatus, an electronic device, and a storage medium. The method includes: obtaining a target image containing face information, and inputting the target image into a pre-constructed pose estimation model; performing feature extraction on the target image by using a shallow densely connected layer to obtain a plurality of first feature maps containing shallow feature information; performing an information fusion operation on the plurality of first feature maps by using a deep feature multiplexing layer to obtain a second feature map, such that deep feature information is fused into the shallow feature information; and extracting face pose information in the second feature map by using an attention layer to obtain a third feature map containing the face pose information, performing prediction on the third feature map by using a classifier, and determining a face pose in the target image.

Abstract: A pedestrian matching method includes: acquiring a first image and a second image; respectively extracting features of the first image and the second image to obtain a first local feature, a second local feature, a first high-order feature and a second high-order feature, the first local feature and the second local feature including local feature vectors corresponding to key points of a human body respectively; performing feature alignment on the first local feature and the second local feature to obtain a first fused feature and a second fused feature; performing feature alignment on the first high-order feature and the second high-order feature to obtain a first high-order fused feature and a second high-order fused feature; and determining, based on the first fused feature, the second fused feature, the first high-order fused feature and the second high-order fused feature, whether the first image and the second image include a same pedestrian.

Abstract: A cigarette device management system includes an information management module, and a maintenance prediction module, a device state acquisition module and a purchase statistics module which are in communication connection with it. The maintenance prediction module is built by an artificial neural network model and is configured to predict a failure interval. The device state acquisition module acquires a device operating parameter and an operating duration of the device in real time, and compares the device operating parameter with a safety parameter range. In accordance with the system, the maintenance timing of the device can be predicted, the purchase quantity of the spare part and the purchase timing of the spare part also can be predicted.