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.

The embodiments of the disclosure provide a video description information generation method, a video processing method, and video description information generation apparatus, and a video processing apparatus. The video description information generation method includes: obtaining a frame-level video feature sequence corresponding to a video; generating a global part-of-speech sequence feature of the video according to the video feature sequence, the global part-of-speech sequence feature being a feature of a sequence of a combination of parts of speech in the video; and generating natural language description information of the video according to the global part-of-speech sequence feature and the video feature sequence.

An image recognition device sets an overall observation region which surrounds a whole body of an object and partial observation regions which surround characteristic parts of the object respectively to locations in an image which are estimated to include captured images of the object. The device clips images in the overall observation region and the partial observation regions, and calculates similarity degrees between them and previously learned images on the basis of a combination of two image feature amounts. The device calculates an optimum ratio in combining the HOG feature amount and the color distribution feature amount individually for the regions. This ratio is determined by setting a weight parameter i for setting a weight used for combining the HOG feature amount and the color distribution feature amount to be included in a state vector and subjecting the result to complete search by a particle filter.

The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium, the method comprising: performing, by a feature extraction network, feature extraction on an image to be processed to obtain a first feature map of the image to be processed; performing, by an M-level encoding network, scale-down and multi-scale fusion processing on the first feature map to obtain a plurality of feature maps which are encoded, each of the plurality of feature maps having a different scale; and performing, by an N-level decoding network, scale-up and multi-scale fusion processing on the plurality of feature maps which are encoded to obtain a prediction result of the image to be processed. Embodiments of the present disclosure are capable of improving the quality and robustness of the prediction result.

An image processing method is provided. The method includes obtaining at least two images, the at least two images being based on the same target object captured from different imaging angles, respectively; extracting, by using feature extraction networks included in an image processing model, target features of the at least two images, the feature extraction networks being configured to extract features of images corresponding to the different imaging angles, respectively; and determining, based on the target features, a classification result corresponding to the target object.

The present disclosure relates to an image processing method and device, an electronic apparatus and a storage medium. The method comprises: performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed; splitting the first feature map into a plurality of first sub-feature maps according to dimension information of the first feature map and a preset splitting rule, wherein the dimension information of the first feature map comprises dimensions of the first feature map and size of each dimension; performing normalization on the plurality of first sub-feature maps respectively to obtain a plurality of second sub-feature maps; and splicing the plurality of second sub-feature maps to obtain a second feature map of the image to be processed. Embodiments of the present disclosure can reduce the statistical errors during normalization of a complete feature map.

The present disclosure relates to a target detection method and device, a computing device and a readable storage medium. The target detection method include performing target detection using a convolutional neural network comprising a plurality of convolutional layers. The method include performing a branch convolutional process on at least one of the convolutional layers to obtain a branch detection result. The method includes performing a fusion process on the branch detection result, or on the branch detection result and a detection result of a last convolutional layer in the convolutional neural network, and transmitting a result of the fusion process to a fully connected layer.

A system generates an output audio signal for an object or virtual object using image data of a room to select a room impulse response from a database. A headset may include a depth camera assembly (DCA) and processing circuitry. The DCA generates depth image data of a room. The processing circuitry determines room parameters such as the dimensions of the room based on the depth image data. A room impulse response for the room is determined based on referencing a database of room impulse responses using the room parameters. An output audio signal is generated by convolving a source audio signal of an object with the room impulse response.

Disclosed in the present disclosure are a driver attention monitoring method and apparatus and an electronic device. The method includes: capturing, by a camera arranged on a vehicle, a video of a driving area of the vehicle; determining, according to each of multiple frames of face images of a driver in the driving area included in the video, a type of a gazing area of the driver in the frame of face image, where the gazing area of each frame of face image is one of multiple types of defined gazing areas obtained by dividing a space area of the vehicle in advance; and determining an attention monitoring result of the driver according to a type distribution of gazing areas of the frames of face images included within at least one sliding time window in the video.

A scene model constructing method includes the following steps. According to multiple position parameters in multiple scene materials, classifying the scene materials into multiple position groups. According to scene similarities between the scene materials, classifying the scene materials into multiple first similar image sub-groups and multiple second similar image sub-groups. Establishing a first similar image sub-model and a second similar image sub-model respectively according to the first similar image sub-group and the second similar image sub-group. Combining a first similar image sub-model to a first position model, and combining a second similar image sub-model to a second position model. Finally, combining the first position model and the second position model to a scene model.