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    PHOTOREALISTIC SYNTHESIS OF AGENTS IN TRAFFIC SCENES
    2.
    发明申请
    PHOTOREALISTIC SYNTHESIS OF AGENTS IN TRAFFIC SCENES 有权

    公开(公告)号：US20250148736A1

    公开(公告)日：2025-05-08

    申请号：US18924258

    申请日：2024-10-23

    Applicant: NEC Laboratories America, Inc.

    Inventor： Bingbing Zhuang ,  Ziyu Jiang ,  Manmohan Chandraker ,  Shanlin Sun

    IPC: G06T19/20 ,  G06T1/20 ,  G06T7/246 ,  G06T7/90 ,  G06T15/00 ,  G06T15/06 ,  G06T15/20 ,  G06T15/50

    Abstract: A computer-implemented method for synthesizing an image includes extracting agent neural radiance fields (NeRFs) from driving video logs and storing agent NeRFs in a database. For a driving video log to be edited, a scene NeRF and agent NeRFs are extracted from the driving video log to be edited. One or more agent NeRFs are selected from the database to insert into or replace existing agents in a traffic scene of the driving video log based on photorealism criteria. The traffic scene is edited by inserting a selected agent NeRF into the traffic scene, replacing existing agents in the traffic scene with the selected agent NeRF, or removing one or more existing agents from the traffic scene. An image of the edited traffic scene is synthesized by composing edited agent NeRFs with the scene NeRF and performing volume rendering.

    OPTIMIZING MODELS FOR OPEN-VOCABULARY DETECTION
    4.
    发明申请
    OPTIMIZING MODELS FOR OPEN-VOCABULARY DETECTION 有权

    公开(公告)号：US20240378454A1

    公开(公告)日：2024-11-14

    申请号：US18659738

    申请日：2024-05-09

    Applicant: NEC Laboratories America, Inc.

    Inventor： Samuel Schulter ,  Yumin Suh ,  Manmohan Chandraker ,  Vijay Kumar Baikampady Gopalkrishna

    IPC: G06N3/096

    Abstract: Systems and methods for optimizing models for open-vocabulary detection. Region proposals can be obtained by employing a pre-trained vision-language model and a pre-trained region proposal network. Object feature predictions can be obtained by employing a trained teacher neural network with the region proposals. Object feature predictions can be filtered above a threshold to obtain pseudo labels. A student neural network with a split-and-fusion detection head can be trained by utilizing the region proposals, base ground truth class labels and the pseudo labels. The pseudo labels can be optimized by reducing the noise from the pseudo labels by employing the trained split-and-fusion detection head of the trained student neural network to obtain optimized object detections. An action can be performed relative to a scene layout based on the optimized object detections.

    Domain adaptation for structured output via disentangled representations
    10.
    发明授权
    Domain adaptation for structured output via disentangled representations 有权

    公开(公告)号：US11604943B2

    公开(公告)日：2023-03-14

    申请号：US16400376

    申请日：2019-05-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yi-Hsuan Tsai ,  Samuel Schulter ,  Kihyuk Sohn ,  Manmohan Chandraker

    IPC: G06K9/62 ,  G06N3/08

    Abstract: Systems and methods for domain adaptation for structured output via disentangled representations are provided. The system receives a ground truth of a source domain. The ground truth is used in a task loss function for a first convolutional neural network that predicts at least one output based on inputs from the source domain and a target domain. The system clusters the ground truth of the source domain into a predetermined number of clusters, and predicts, via a second convolutional neural network, a structure of label patches. The structure includes an assignment of each of the at least one output of the first convolutional neural network to the predetermined number of clusters. A cluster loss is computed for the predicted structure of label patches, and an adversarial loss function is applied to the predicted structure of label patches to align the source domain and the target domain on a structural level.

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