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公开(公告)号：US20190102908A1
公开(公告)日：2019-04-04
申请号：US16152303
申请日：2018-10-04
Applicant: NVIDIA Corporation
Inventor： Xiaodong YANG , Xitong YANG , Fanyi XIAO , Ming-Yu LIU , Jan KAUTZ
IPC: G06T7/73
Abstract: Iterative prediction systems and methods for the task of action detection process an inputted sequence of video frames to generate an output of both action tubes and respective action labels, wherein the action tubes comprise a sequence of bounding boxes on each video frame. An iterative predictor processes large offsets between the bounding boxes and the ground-truth.
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公开(公告)号：US20210241489A1
公开(公告)日：2021-08-05
申请号：US17237728
申请日：2021-04-22
Applicant: NVIDIA Corporation
Inventor： Xiaodong YANG , Ming-Yu LIU , Jan KAUTZ , Fanyi XIAO , Xitong YANG
Abstract: Iterative prediction systems and methods for the task of action detection process an inputted sequence of video frames to generate an output of both action tubes and respective action labels, wherein the action tubes comprise a sequence of bounding boxes on each video frame. An iterative predictor processes large offsets between the bounding boxes and the ground-truth.
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公开(公告)号：US20240303494A1
公开(公告)日：2024-09-12
申请号：US18666613
申请日：2024-05-16
Applicant: NVIDIA Corporation
Inventor： Ming-Yu LIU , Xun HUANG , Tero Tapani KARRAS , Timo AILA , Jaakko LEHTINEN
IPC: G06N3/088 , G06F18/214 , G06F18/2431 , G06T3/02 , G06T3/60 , G06T7/73 , G06V10/764 , G06V10/82
CPC classification number: G06N3/088 , G06F18/214 , G06F18/2431 , G06T3/02 , G06T3/60 , G06T7/74 , G06V10/764 , G06V10/82 , G06T2207/20081 , G06T2207/20084
Abstract: A few-shot, unsupervised image-to-image translation (“FUNIT”) algorithm is disclosed that accepts as input images of previously-unseen target classes. These target classes are specified at inference time by only a few images, such as a single image or a pair of images, of an object of the target type. A FUNIT network can be trained using a data set containing images of many different object classes, in order to translate images from one class to another class by leveraging few input images of the target class. By learning to extract appearance patterns from the few input images for the translation task, the network learns a generalizable appearance pattern extractor that can be applied to images of unseen classes at translation time for a few-shot image-to-image translation task.
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Patent Agency Ranking
公开(公告)号：US20220374637A1
公开(公告)日：2022-11-24
申请号：US17326091
申请日：2021-05-20
Applicant: Nvidia Corporation
Inventor： Ming-Yu LIU , Ting-Chun WANG , Arun MALLYA
Abstract: Apparatuses, systems, and techniques are presented to reduce an amount of data to be transmitted for media content. In at least one embodiment, one or more neural networks are used to generate video and audio information corresponding to one or more people based, at least in part, on at least one image and voice information corresponding to the one or more people.
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公开(公告)号：US20220335672A1
公开(公告)日：2022-10-20
申请号：US17585449
申请日：2022-01-26
Applicant: NVIDIA Corporation
Inventor： Donghoon LEE , Sifei LIU , Jinwei GU , Ming-Yu LIU , Jan KAUTZ
IPC: G06T11/60 , G06T3/00 , G06K9/62 , G06T7/30 , G06V30/262
Abstract: One embodiment of a method includes applying a first generator model to a semantic representation of an image to generate an affine transformation, where the affine transformation represents a bounding box associated with at least one region within the image. The method further includes applying a second generator model to the affine transformation and the semantic representation to generate a shape of an object. The method further includes inserting the object into the image based on the bounding box and the shape.
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公开(公告)号：US20240161250A1
公开(公告)日：2024-05-16
申请号：US18485239
申请日：2023-10-11
Applicant: NVIDIA CORPORATION
Inventor： Yogesh BALAJI , Timo Oskari AILA , Miika AITTALA , Bryan CATANZARO , Xun HUANG , Tero Tapani KARRAS , Karsten KREIS , Samuli LAINE , Ming-Yu LIU , Seungjun NAH , Jiaming SONG , Arash VAHDAT , Qinsheng ZHANG
IPC: G06T5/00
CPC classification number: G06T5/002 , G06T2207/20081 , G06T2207/20084
Abstract: Techniques are disclosed herein for generating a content item. The techniques include performing one or more first denoising operations based on an input and a first machine learning model to generate a first content item, and performing one or more second denoising operations based on the input, the first content item, and a second machine learning model to generate a second content item, where the first machine learning model is trained to denoise content items having an amount of corruption within a first corruption range, the second machine learning model is trained to denoise content items having an amount of corruption within a second corruption range, and the second corruption range is lower than the first corruption range.
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公开(公告)号：US20230274472A1
公开(公告)日：2023-08-31
申请号：US18140321
申请日：2023-04-27
Applicant: NVIDIA Corporation
Inventor： Xihui LIU , Ming-Yu LIU , Ting-Chun WANG
IPC: G06T11/00 , G06T7/70 , G06T7/40 , G06N3/0455 , G06N3/08
CPC classification number: G06T11/001 , G06T7/70 , G06T7/40 , G06N3/0455 , G06N3/08 , G06T2207/20084 , G06T2207/30196 , G06T2207/10024
Abstract: Apparatuses, systems, and techniques are presented to generate one or more images. In at least one embodiment, one or more neural networks are used to generate one or more images of one or more objects based, at least in part, on a model of the one or more objects and texture information.
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公开(公告)号：US20200074707A1
公开(公告)日：2020-03-05
申请号：US16201934
申请日：2018-11-27
Applicant: NVIDIA CORPORATION
Inventor： Donghoon LEE , Sifei LIU , Jinwei GU , Ming-Yu LIU , Jan KAUTZ
Abstract: One embodiment of a method includes applying a first generator model to a semantic representation of an image to generate an affine transformation, where the affine transformation represents a bounding box associated with at least one region within the image. The method further includes applying a second generator model to the affine transformation and the semantic representation to generate a shape of an object. The method further includes inserting the object into the image based on the bounding box and the shape.
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