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    TEMPORAL-BASED PERCEPTION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
    11.
    发明公开
    TEMPORAL-BASED PERCEPTION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS 审中-公开

    公开(公告)号：US20240312219A1

    公开(公告)日：2024-09-19

    申请号：US18185074

    申请日：2023-03-16

    Applicant: NVIDIA Corporation

    Inventor： Jiwoong Choi ,  Jose Manuel Alvarez Lopez ,  Shiyi Lan ,  Yashar Asgarieh ,  Zhiding Yu

    IPC: G06V20/58 ,  B60W60/00

    CPC classification number: G06V20/58 ,  B60W60/001 ,  B60W2420/403

    Abstract: In various examples, temporal-based perception for autonomous or semi-autonomous systems and applications is described. Systems and methods are disclosed that use a machine learning model (MLM) to intrinsically fuse feature maps associated with different sensors and different instances in time. To generate a feature map, image data generated using image sensors (e.g., cameras) located around a vehicle are processed using a MLM that is trained to generate the feature map. The MLM may then fuse the feature maps in order to generate a final feature map associated with a current instance in time. The feature maps associated with the previous instances in time may be preprocessed using one or more layers of the MLM, where the one or more layers are associated with performing temporal transformation before the fusion is performed. The MLM may then use the final feature map to generate one or more outputs.

    IMAGE PROCESSING USING COUPLED SEGMENTATION AND EDGE LEARNING
    14.
    发明申请
    IMAGE PROCESSING USING COUPLED SEGMENTATION AND EDGE LEARNING 有权

    公开(公告)号：US20230015989A1

    公开(公告)日：2023-01-19

    申请号：US17365877

    申请日：2021-07-01

    Applicant: Nvidia Corporation

    Inventor： Zhiding Yu ,  Rui Huang ,  Wonmin Byeon ,  Sifei Liu ,  Guilin Liu ,  Thomas Breuel ,  Anima Anandkumar ,  Jan Kautz

    IPC: G06K9/46 ,  G06N3/04 ,  G06T7/13 ,  G06K9/62

    Abstract: The disclosure provides a learning framework that unifies both semantic segmentation and semantic edge detection. A learnable recurrent message passing layer is disclosed where semantic edges are considered as explicitly learned gating signals to refine segmentation and improve dense prediction quality by finding compact structures for message paths. The disclosure includes a method for coupled segmentation and edge learning. In one example, the method includes: (1) receiving an input image, (2) generating, from the input image, a semantic feature map, an affinity map, and a semantic edge map from a single backbone network of a convolutional neural network (CNN), and (3) producing a refined semantic feature map by smoothing pixels of the semantic feature map using spatial propagation, and controlling the smoothing using both affinity values from the affinity map and edge values from the semantic edge map.

    IMAGE IDENTIFICATION USING NEURAL NETWORKS
    17.
    发明申请
    IMAGE IDENTIFICATION USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200302176A1

    公开(公告)日：2020-09-24

    申请号：US16357047

    申请日：2019-03-18

    Applicant: NVIDIA Corporation

    Inventor： Xiaodong Yang ,  Zhedong Zheng ,  Zhiding Yu

    IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06F7/57

    Abstract: A neural network is trained to perform a re-identification task in which it is determined whether one or more features present in a first image appear also in a second image. During training, a generative portion of one or more neural networks generates variations of an input image, and a discriminative portion of the one or more neural networks learns to perform the re-identification task based at least in part on the variations of the image. During training, the generative and discriminative portions of the one or more neural networks share an encoder which encodes information used by the generative and discriminative portions.

    AUTO-LABELING SYSTEMS AND APPLICATIONS FOR OPEN-SET AND OUT-OF-DOMAIN SEGMENTATION
    18.
    发明申请
    AUTO-LABELING SYSTEMS AND APPLICATIONS FOR OPEN-SET AND OUT-OF-DOMAIN SEGMENTATION 有权

    公开(公告)号：US20250029409A1

    公开(公告)日：2025-01-23

    申请号：US18354431

    申请日：2023-07-18

    Applicant: Nvidia Corporation

    Inventor： Subhashree Radhakrishnan ,  Ramanathan Arunachahalam ,  Farzin Aghdasi ,  Zhiding Yu ,  Shiyi Lan

    IPC: G06V20/70 ,  G06V10/82

    Abstract: Approaches are disclosed herein for an automatic segmentation labeling system that identifies objects for potential open-class categories and generates segmentation masks for objects. The disclosed system may use a training pipeline that trains two segmentation models. The training pipeline may take, as input, a set of images with bounding boxes and class labels. The set of images may be fed into a first segmentation network with the bounding boxes used as ground truth for weak supervision. The first segmentation network may be trained to generate pseudo segmentation masks. In a second stage, the trained first segmentation network is used to generate pseudo masks for a set of input images. The generated pseudo masks are provided as input, along with the corresponding images, to a second segmentation network to be used as a type of ground truth data for training the second segmentation network to generate high-quality segmentation masks.

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