Abstract: A method of establishing an enhanced three-dimensional (3D) model of intracranial angiography is provided and includes: obtaining a bright-blood image group, a black-blood image group and an enhanced black-blood image group; preprocessing image pairs to obtain first bright-blood images and black-blood images; registering the first bright-blood image by taking the first black-blood image as reference to obtain a registered bright-blood image group; eliminating flowing void artifact to obtain an artifact-elimination enhanced black-blood image group; subtracting each image of the artifact-elimination enhanced black-blood image group from corresponding black-blood image to obtain angiography enhanced images; establishing a blood 3D model and a vascular 3D model with blood boundary expansion by using the registered bright-blood image group; establishing an angiography enhanced 3D model by using the angiography enhanced images; obtaining an enhanced 3D model of intracranial angiography based on the blood 3D model,

Abstract: Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, and computer program products for using an image of an integrated circuit (IC) including a plurality of cells to locate one or more target cells within the IC. Accordingly, in various embodiments, a footprint for each cell of the plurality of cells is encoded to transform the image of the IC into a two-dimensional string matrix. A string search algorithm is then applied on each encoded dopant region found in the two-dimensional string matrix using an encoded target layout cell to identify one or more candidate regions of interest within the image. Finally, a mask window is slid over each candidate region of interest while performing matching using match criteria to identify any target cells in the one or more target cells that are located within the candidate region of interest.

Abstract: Methods and apparatus for providing a representation of an environment, for example, in an XR system, and any suitable computer vision and robotics applications. A representation of an environment may include one or more planar features. The representation of the environment may be provided by jointly optimizing plane parameters of the planar features and sensor poses that the planar features are observed at. The joint optimization may be based on a reduced matrix and a reduced residual vector in lieu of the Jacobian matrix and the original residual vector.

Abstract: A method for employing facial information in unsupervised person re-identification is presented. The method includes extracting, by a body feature extractor, body features from a first data stream, extracting, by a head feature extractor, head features from a second data stream, outputting a body descriptor vector from the body feature extractor, outputting a head descriptor vector from the head feature extractor, and concatenating the body descriptor vector and the head descriptor vector to enable a model to generate a descriptor vector.

Abstract: A computer-implemented method of training an image generation model. The image generation model comprises an argmax transformation configured to compute a discrete index feature indicating an index of a feature of the continuous feature vector with an extreme value. The image generation model is trained using a log-likelihood optimization. This involves obtaining a value of the index feature for the training image, sampling values of the continuous feature vector given the value of the index feature according to a stochastic inverse transformation of the argmax transformation, and determining a likelihood contribution of the argmax transformation for the log-likelihood based on a probability that the stochastic inverse transformation generates the values of the continuous feature vector given the value of the index feature.

Abstract: Certain aspects of the present disclosure provide a method, including: processing input data with a feature extraction stage of a machine learning model to generate a feature map; applying an attention map to the feature map to generate an augmented feature map; processing the augmented feature map with a refinement stage of the machine learning model to generate a refined feature map; processing the refined feature map with a first regression stage of the machine learning model to generate multi-dimensional task output data; and processing the refined feature data with an attention stage of the machine learning model to generate an updated attention map.