Disclosed are multiple object detection method and apparatus. The multiple object detection apparatus includes a feature map extraction unit for extracting a plurality of multi-scale feature maps based on an input image, and a feature map fusion unit for generating a multi-scale fusion feature map including context information by fusing adjacent multi-scale feature maps among the plurality of multi-scale feature maps generated by the feature map extraction unit.

An expression recognition method and apparatus, a computer device, and a readable storage medium are provided. The method includes: performing face key-point position detection on a face image to obtain face key-point position information; and obtaining expression class information of the face image using four cascaded convolutional modules and a trained neural network classifier according to the face image and the face key-point position information.

An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The text includes errors associated with noise in the image. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the errors from the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

The present application is suitable for use in the technical field of computers, and provides a smart diagnosis assistance method and terminal based on medical images, comprising: acquiring a medical image to be classified; pre-processing the medical image to be classified to obtain a pre-processed image; and inputting the pre-processed image into a trained classification model for classification processing to obtain a classification type corresponding to the pre-processed image, the classification model comprising tensorized network layers and a second-order pooling module. As the trained classification model comprises tensor decomposed network layers and a second-order pooling module, when processing images on the basis of the classification model, more discriminative features related to pathologies can be extracted, increasing the accuracy of medical image classification.

An example apparatus for re-identifying objects includes an image receiver to receive a first image and a second image of an object with an identity. The apparatus also includes a fused model generator to fuse a global representation of the object with local representations of pose parts of the object to generate a fused representation of the object based on the first image. The apparatus further includes an object re-identifier to re-identify the object with the identity in the second image based on the fused representation.

The disclosure provides a classification method and an electronic apparatus. The classification method includes the following steps. First feature data of multiple pictures of assembly is extracted, and each picture of assembly includes an operator at a station. The first feature data is converted into a first feature vector. Second feature data recording personal data of the operator is converted into a second feature vector. The first feature vector and the second feature vector are merged into a first feature matrix. The efficiency of the operator operating at the station is classified according to the first feature matrix to obtain a classification result.

An end-to-end multimodal gait recognition method based on deep learning includes: first extracting gait appearance features (color, texture and the like) through RGB video frames, and obtaining a mask by semantic segmentation of the RGB video frames; then extracting gait mask features (contour and the like) through the mask; and finally performing fusion and recognition on the two kinds of features. The method is configured for extracting gait appearance feature and mask feature by improving GaitSet, improving semantic segmentation speed on the premise of ensuring accuracy through simplified FCN, and fusing the gait appearance feature and the mask feature to obtain a more complete information representation.

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

An object recognition method includes extracting, by a first Transformer network, spatial features of a plurality of medical images respectively, the plurality of medical images being images of a same object at different times, and fusing the extracted plurality of spatial features, to obtain a first fusion spatial feature of the object. The method further includes extracting, by a second Transformer network, a spatial-temporal feature of the object based on the first fusion spatial feature. The spatial-temporal feature indicates a change in the spatial features of the plurality of medical images at the different times. The method further includes recognizing a state of the object based on the spatial-temporal feature, to obtain a recognition result of the 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.