The present disclosure provides a deep learning based medical image detection method and apparatus, a computer-readable medium, and an electronic device. The method includes: acquiring a to-be-detected medical image comprising a plurality of slices; for each slice in the to-be-detected medical image: extracting N basic feature maps of the slice by a deep neural network, N being an integer greater than 1, merging features of the N basic feature maps by the deep neural network, to obtain M enhanced feature maps, M being an integer greater than 1, and respectively performing a hierarchically dilated convolutions operation on the M enhanced feature maps by the deep neural network, to generate a superposed feature map of each enhanced feature map; and predicting position information of a region of interest and a confidence score thereof in the to-be-detected medical image by the deep neural network based on the superposed feature map.

Embodiments of this application provide a method for making recommendations to a user and an apparatus, a computing device, and a storage medium. The method includes obtaining user attribute information, reading attribute information, reading history information, and candidate items; performing intra-group information fusion on the reading attribute information according to preset groupings to obtain reading feature information; obtaining a reading history weight according to the reading history information; obtaining history feature information according to the reading history weight and the reading history information; obtaining user feature information according to the user attribute information, the reading feature information, and the history feature information; and selecting a recommendation item from the candidate items according to the user feature information.

An image processing method includes: obtaining an image, and performing feature extraction on the image; generating at least one candidate region of the image, and mapping the at least one candidate region into a feature map of the image, one candidate region including one instance; processing the mapped feature map based on a target network for instance segmentation; and indicating an overall structure of an occluded instance in the image by using a perspective mask, and indicating an invisible part of the occluded instance by using a non-perspective mask, the perspective mask and the non-perspective mask representing a relative occlusion relationship of the occluded instance.

This application relates to a user tag generation method performed by a computer device, relating to the field of neural networks. The method includes: obtaining discrete user data corresponding to a target user identifier in multiple feature fields respectively; for each feature field, obtaining an intra-field feature corresponding to the target user identifier according to the discrete user data in the feature field; merging the intra-field features to obtain an inter-field feature corresponding to the target user identifier; performing feature crossing on sub-features in the inter-field feature to obtain a cross feature corresponding to the target user identifier; and selecting, from candidate user tags, a target user tag corresponding to the target user identifier according to the inter-field feature and the cross feature. The solutions provided by this application can improve the accuracy of generating user tags.

A video recommendation method is provided, including: inputting a video to a first feature extraction network, performing feature extraction on at least one consecutive video frame in the video, and outputting a video feature of the video; inputting user data of a user to a second feature extraction network, performing feature extraction on the discrete user data, and outputting a user feature of the user; performing feature fusion based on the video feature and the user feature, and obtaining a recommendation probability of recommending the video to the user; and determining, according to the recommendation probability, whether to recommend the video to the user.

Provided are a video processing method and apparatus, an electronic device, and a storage medium. The video processing method includes: acquiring at least one candidate video frame sequence; performing intra-sequence frame selection on each candidate video frame sequence to obtain a first frame selection result respectively corresponding to each candidate video frame sequence; and performing global frame selection based on all the first frame selection results to obtain a final frame selection result.

A method and device of multi-focal sensing of an obstacle. A method includes acquiring detection results of obstacles at multiple moments by utilizing a camera with long focus lens and a camera with short focus lens; performing a target tracking to the acquired detection results, to obtain at least two tracking sequences, wherein each tracking sequence includes detection results acquired at the multiple moments for a same obstacle; and matching two random tracking sequences of the at least two tracking sequences, and combining the two random tracking sequences into a combined tracking sequence, if the matching is successful.

According to the embodiments of the present application, there are proposed a method and electronic device for retrieving an image, and computer readable storage medium. The method includes: processing an image to be retrieved using a first neural network to determine a local feature vector of the image to be retrieved; processing the image to be retrieved using a second neural network to determine a global feature vector of the image to be retrieved; and determining, based on the local feature vector and the global feature vector, an image having a similarity to the image to be retrieved which is higher than a similarity threshold.

A method, a system and an electronic device for processing audio-visual data. In the method, a first dataset is obtained, where the first dataset includes several data pairs, and each of the data pairs in the first dataset includes a video frame and an audio clip that match each other. A multi-channel feature extraction network model is established to extract the visual features of each video frame and the auditory features of each audio clip in the first dataset. A contrastive loss function model is established using the extracted visual features and the auditory features to train the multi-channel feature extraction network. A classifier is established to determine whether an input audio-visual data pair is matched.

Various embodiments dynamically learn user-customizable input gestures. A user can transition a radar-based gesture detection system into a gesture-learning mode. In turn, the radar-based gesture detection system emits a radar field configured to detect a gesture new to the radar-based gesture detection system. The radar-based gesture detection system receives incoming radio frequency (RF) signals generated by the outgoing RF signal reflecting off the gesture, and analyzes the incoming RF signals to learn one or more identifying characteristics about the gesture. Upon learning the identifying characteristics, the radar-based gesture detection system reconfigures a corresponding input identification system to detect the gesture when the one or more identifying characteristics are next identified, and transitions out of the gesture-learning mode.