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    Training a neural network to predict superpixels using segmentation-aware affinity loss
    81.
    发明授权
    Training a neural network to predict superpixels using segmentation-aware affinity loss 有权

    公开(公告)号：US11256961B2

    公开(公告)日：2022-02-22

    申请号：US16921012

    申请日：2020-07-06

    Applicant: NVIDIA Corporation

    Inventor： Wei-Chih Tu ,  Ming-Yu Liu ,  Varun Jampani ,  Deqing Sun ,  Ming-Hsuan Yang ,  Jan Kautz

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11

    Abstract: Segmentation is the identification of separate objects within an image. An example is identification of a pedestrian passing in front of a car, where the pedestrian is a first object and the car is a second object. Superpixel segmentation is the identification of regions of pixels within an object that have similar properties. An example is identification of pixel regions having a similar color, such as different articles of clothing worn by the pedestrian and different components of the car. A pixel affinity neural network (PAN) model is trained to generate pixel affinity maps for superpixel segmentation. The pixel affinity map defines the similarity of two points in space. In an embodiment, the pixel affinity map indicates a horizontal affinity and vertical affinity for each pixel in the image. The pixel affinity map is processed to identify the superpixels.

    SELF-SUPERVISED HIERARCHICAL MOTION LEARNING FOR VIDEO ACTION RECOGNITION
    85.
    发明申请
    SELF-SUPERVISED HIERARCHICAL MOTION LEARNING FOR VIDEO ACTION RECOGNITION 有权

    公开(公告)号：US20210064931A1

    公开(公告)日：2021-03-04

    申请号：US16998914

    申请日：2020-08-20

    Applicant: NVIDIA Corporation

    Inventor： Xiaodong Yang ,  Xitong Yang ,  Sifei Liu ,  Jan Kautz

    IPC: G06K9/62 ,  G06K9/72 ,  G06K9/00 ,  G06N3/08 ,  G06N3/04

    Abstract: There are numerous features in video that can be detected using computer-based systems, such as objects and/or motion. The detection of these features, and in particular the detection of motion, has many useful applications, such as action recognition, activity detection, object tracking, etc. The present disclosure provides a neural network that learns motion from unlabeled video frames. In particular, the neural network uses the unlabeled video frames to perform self-supervised hierarchical motion learning. The present disclosure also describes how the learned motion can be used in video action recognition.

    JOINT REPRESENTATION LEARNING FROM IMAGES AND TEXT
    86.
    发明申请
    JOINT REPRESENTATION LEARNING FROM IMAGES AND TEXT 有权

    公开(公告)号：US20210056353A1

    公开(公告)日：2021-02-25

    申请号：US17000048

    申请日：2020-08-21

    Applicant: Nvidia Corporation

    Inventor： Arash Vahdat ,  Tanmay Gupta ,  Xiaodong Yang ,  Jan Kautz

    IPC: G06K9/62 ,  G06N3/08

    Abstract: The disclosure provides a framework or system for learning visual representation using a large set of image/text pairs. The disclosure provides, for example, a method of visual representation learning, a joint representation learning system, and an artificial intelligence (AI) system that employs one or more of the trained models from the method or system. The AI system can be used, for example, in autonomous or semi-autonomous vehicles. In one example, the method of visual representation learning includes: (1) receiving a set of image embeddings from an image representation model and a set of text embeddings from a text representation model, and (2) training, employing mutual information, a critic function by learning relationships between the set of image embeddings and the set of text embeddings.

    Learning based camera pose estimation from images of an environment
    89.
    发明授权
    Learning based camera pose estimation from images of an environment 有权

    公开(公告)号：US10692244B2

    公开(公告)日：2020-06-23

    申请号：US16137064

    申请日：2018-09-20

    Applicant: NVIDIA Corporation

    Inventor： Jinwei Gu ,  Samarth Manoj Brahmbhatt ,  Kihwan Kim ,  Jan Kautz

    IPC: G06T7/80 ,  G06T7/00 ,  G06K9/00 ,  G06K9/20 ,  G06K9/46 ,  G06N3/00 ,  G06T7/579 ,  G06T7/20

    Abstract: A deep neural network (DNN) system learns a map representation for estimating a camera position and orientation (pose). The DNN is trained to learn a map representation corresponding to the environment, defining positions and attributes of structures, trees, walls, vehicles, etc. The DNN system learns a map representation that is versatile and performs well for many different environments (indoor, outdoor, natural, synthetic, etc.). The DNN system receives images of an environment captured by a camera (observations) and outputs an estimated camera pose within the environment. The estimated camera pose is used to perform camera localization, i.e., recover the three-dimensional (3D) position and orientation of a moving camera, which is a fundamental task in computer vision with a wide variety of applications in robot navigation, car localization for autonomous driving, device localization for mobile navigation, and augmented/virtual reality.

    3D PLANE DETECTION AND RECONSTRUCTION USING A MONOCULAR IMAGE
    90.
    发明申请
    3D PLANE DETECTION AND RECONSTRUCTION USING A MONOCULAR IMAGE 审中-公开

    公开(公告)号：US20200167943A1

    公开(公告)日：2020-05-28

    申请号：US16565885

    申请日：2019-09-10

    Applicant: NVIDIA Corporation

    Inventor： Kihwan Kim ,  Jinwei Gu ,  Chen Liu ,  Jan Kautz

    IPC: G06T7/593 ,  G06T7/11 ,  G06T7/143 ,  G06N3/04

    Abstract: Planar regions in three-dimensional scenes offer important geometric cues in a variety of three-dimensional perception tasks such as scene understanding, scene reconstruction, and robot navigation. Image analysis to detect planar regions can be performed by a deep learning architecture that includes a number of neural networks configured to estimate parameters for the planar regions. The neural networks process an image to detect an arbitrary number of plane objects in the image. Each plane object is associated with a number of estimated parameters including bounding box parameters, plane normal parameters, and a segmentation mask. Global parameters for the image, including a depth map, can also be estimated by one of the neural networks. Then, a segmentation refinement network jointly optimizes (i.e., refines) the segmentation masks for each instance of the plane objects and combines the refined segmentation masks to generate an aggregate segmentation mask for the image.

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