LEARNING PRIVACY-PRESERVING OPTICS VIA ADVERSARIAL TRAINING

    公开(公告)号:US20220067457A1

    公开(公告)日:2022-03-03

    申请号:US17412704

    申请日:2021-08-26

    Abstract: A method for acquiring privacy-enhancing encodings in an optical domain before image capture is presented. The method includes feeding a differentiable sensing model with a plurality of images to obtain encoded images, the differentiable sensing model including parameters for sensor optics, integrating the differentiable sensing model into an adversarial learning framework where parameters of attack networks, parameters of utility networks, and the parameters of the sensor optics are concurrently updated, and, once adversarial training is complete, validating efficacy of a learned sensor design by fixing the parameters of the sensor optics and training the attack networks and the utility networks to learn to estimate private and public attributes, respectively, from a set of the encoded images.

    MONOCULAR 3D OBJECT LOCALIZATION FROM TEMPORAL AGGREGATION

    公开(公告)号:US20220063605A1

    公开(公告)日:2022-03-03

    申请号:US17408911

    申请日:2021-08-23

    Abstract: A method provided for 3D object localization predicts pairs of 2D bounding boxes. Each pair corresponds to a detected object in each of the two consecutive input monocular images. The method generates, for each detected object, a relative motion estimation specifying a relative motion between the two images. The method constructs an object cost volume by aggregating temporal features from the two images using the pairs of 2D bounding boxes and the relative motion estimation to predict a range of object depth candidates and a confidence score for each object depth candidate and an object depth from the object depth candidates. The method updates the relative motion estimation based on the object cost volume and the object depth to provide a refined object motion and a refined object depth. The method reconstructs a 3D bounding box for each detected object based on the refined object motion and refined object depth.

    Viewpoint invariant object recognition by synthesization and domain adaptation

    公开(公告)号:US11055989B2

    公开(公告)日:2021-07-06

    申请号:US16051924

    申请日:2018-08-01

    Abstract: Systems and methods for performing domain adaptation include collecting a labeled source image having a view of an object. Viewpoints of the object in the source image are synthesized to generate view augmented source images. Photometrics of each of the viewpoints of the object are adjusted to generate lighting and view augmented source images. Features are extracted from each of the lighting and view augmented source images with a first feature extractor and from captured images captured by an image capture device with a second feature extractor. The extracted features are classified using domain adaptation with domain adversarial learning between extracted features of the captured images and extracted features of the lighting and view augmented source images. Labeled target images are displayed corresponding to each of the captured images including labels corresponding to classifications of the extracted features of the captured images.

    PARAMETRIC TOP-VIEW REPRESENTATION OF COMPLEX ROAD SCENES

    公开(公告)号:US20210150203A1

    公开(公告)日:2021-05-20

    申请号:US17096111

    申请日:2020-11-12

    Abstract: Systems and methods are provided for producing a road layout model. The method includes capturing digital images having a perspective view, converting each of the digital images into top-down images, and conveying a top-down image of time t to a neural network that performs a feature transform to form a feature map of time t. The method also includes transferring the feature map of the top-down image of time t to a feature transform module to warp the feature map to a time t+1, and conveying a top-down image of time t+1 to form a feature map of time t+1. The method also includes combining the warped feature map of time t with the feature map of time t+1 to form a combined feature map, transferring the combined feature map to a long short-term memory (LSTM) module to generate the road layout model, and displaying the road layout model.

    Pose-variant 3D facial attribute generation

    公开(公告)号:US10991145B2

    公开(公告)日:2021-04-27

    申请号:US16673256

    申请日:2019-11-04

    Abstract: A system is provided for pose-variant 3D facial attribute generation. A first stage has a hardware processor based 3D regression network for directly generating a space position map for a 3D shape and a camera perspective matrix from a single input image of a face and further having a rendering layer for rendering a partial texture map of the single input image based on the space position map and the camera perspective matrix. A second stage has a hardware processor based two-part stacked Generative Adversarial Network (GAN) including a Texture Completion GAN (TC-GAN) stacked with a 3D Attribute generation GAN (3DA-GAN). The TC-GAN completes the partial texture map to form a complete texture map based on the partial texture map and the space position map. The 3DA-GAN generates a target facial attribute for the single input image based on the complete texture map and the space position map.

Patent Agency Ranking