Techniques for Interactive Image Segmentation Networks

    公开(公告)号:US20210225002A1

    公开(公告)日:2021-07-22

    申请号:US17161139

    申请日:2021-01-28

    Abstract: Various embodiments are generally directed to techniques for image segmentation utilizing context, such as with a machine learning (ML) model that injects context into various training stages. Many embodiments utilize one or more of an encoder-decoder model topology and select criteria and parameters in hyper-parameter optimization (HPO) to conduct the best model neural architecture search (NAS). Some embodiments are particularly directed to resizing context frames to a resolution that corresponds with a particular stage of decoding. In several embodiments, the context frames are concatenated with one or more of data from a previous decoding stage and data from a corresponding encoding stage prior to being provided as input to a next decoding stage.

    Techniques for interactive image segmentation networks

    公开(公告)号:US12112482B2

    公开(公告)日:2024-10-08

    申请号:US17161139

    申请日:2021-01-28

    CPC classification number: G06T7/11 G06N3/045 G06T2207/20081 G06T2207/20084

    Abstract: Various embodiments are generally directed to techniques for image segmentation utilizing context, such as with a machine learning (ML) model that injects context into various training stages. Many embodiments utilize one or more of an encoder-decoder model topology and select criteria and parameters in hyper-parameter optimization (HPO) to conduct the best model neural architecture search (NAS). Some embodiments are particularly directed to resizing context frames to a resolution that corresponds with a particular stage of decoding. In several embodiments, the context frames are concatenated with one or more of data from a previous decoding stage and data from a corresponding encoding stage prior to being provided as input to a next decoding stage.

    Gesture-controlled virtual reality systems and methods of controlling the same

    公开(公告)号:US11347319B2

    公开(公告)日:2022-05-31

    申请号:US17074038

    申请日:2020-10-19

    Abstract: Gesture-controlled virtual reality systems and methods of controlling the same are disclosed herein. An example apparatus includes an on-body sensor to output first signals associated with at least one of movement of a body part of a user or a position of the body part relative to a virtual object and an off-body sensor to output second signals associated with at least one of the movement or the position relative to the virtual object. The apparatus also includes at least one processor to generate gesture data based on at least one of the first or second signals, generate position data based on at least one of the first or second signals, determine an intended action of the user relative to the virtual object based on the position data and the gesture data, and generate an output of the virtual object in response to the intended action.

    Video tracking with deep Siamese networks and Bayesian optimization

    公开(公告)号:US11227179B2

    公开(公告)日:2022-01-18

    申请号:US16586671

    申请日:2019-09-27

    Abstract: An apparatus, method, system and computer readable medium for video tracking. An exemplar crop is selected to be tracked in an initial frame of a video. Bayesian optimization is applied with each subsequent frame of the video by building a surrogate model of an objective function using Gaussian Process Regression (GPR) based on similarity scores of candidate crops collected from a search space in a current frame of the video. A next candidate crop in the search space is determined using an acquisition function. The next candidate crop is compared to the exemplar crop using a Siamese neural network. Comparisons of new candidate crops to the exemplar crop are made using the Siamese neural network until the exemplar crop has been found in the current frame. The new candidate crops are selected based on an updated surrogate model.

    HIGH RESOLUTION INTERACTIVE VIDEO SEGMENTATION USING LATENT DIVERSITY DENSE FEATURE DECOMPOSITION WITH BOUNDARY LOSS

    公开(公告)号:US20210150329A1

    公开(公告)日:2021-05-20

    申请号:US16683326

    申请日:2019-11-14

    Abstract: Methods, systems and apparatuses may provide for technology that trains a neural network by inputting video data to the neural network, determining a boundary loss function for the neural network, and selecting weights for the neural network based at least in part on the boundary loss function, wherein the neural network outputs a pixel-level segmentation of one or more objects depicted in the video data. The technology may also operate the neural network by accepting video data and an initial feature set, conducting a tensor decomposition on the initial feature set to obtain a reduced feature set, and outputting a pixel-level segmentation of object(s) depicted in the video data based at least in part on the reduced feature set.

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