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公开(公告)号:US20220138500A1
公开(公告)日:2022-05-05
申请号:US17512312
申请日:2021-10-27
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Aleksai LEVINSHTEIN , Xinyu SUN , Haicheng WANG , Vineeth Subrahmanya BHASKARA , Stavros TSOGKAS , Allan JEPSON
Abstract: A method for training a super-resolution network may include obtaining a low resolution image; generating, using a first machine learning model, a first high resolution image based on the low resolution image; generating, using a second machine learning model, a second high resolution image based on the first high resolution image and an unpaired dataset of high resolution images; obtaining a training data set using the low resolution image and the second high resolution image; and training the super-resolution network using the training data set.
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公开(公告)号:US20230394079A1
公开(公告)日:2023-12-07
申请号:US18453838
申请日:2023-08-22
Applicant: Samsung Electronics Co., Ltd.
Inventor: Haotian ZHANG , Allan JEPSON , Iqbal Ismail MOHOMED , Konstantinos DERPANIS , Ran ZHANG , Afsaneh FAZLY
IPC: G06F16/535 , G06N20/00
CPC classification number: G06F16/535 , G06N20/00
Abstract: A method of personalized image retrieval includes obtaining a natural language query including a name; replacing the name in the natural language query with a generic term to provide an anonymized query and named entity information; obtaining a plurality of initial ranking scores and a plurality of attention weights corresponding to a plurality of images using a trained scoring model that inputs the anonymized query and the plurality of images; obtaining a plurality of delta scores corresponding to the plurality of images using a re-scoring model that inputs the plurality of attention weights and the named entity information; and obtaining a plurality of final ranking scores by modifying the plurality of initial ranking scores based on the plurality of delta scores. The trained scoring model performs semantic based searching and the re-scoring model determines a probability that faces detected in the plurality of images correspond to the name.
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公开(公告)号:US20210192693A1
公开(公告)日:2021-06-24
申请号:US16725717
申请日:2019-12-23
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Allan JEPSON , Aleksai Levinshtein , Mete Kemertas , Haotian Zhang , Hoda Rezaee Kaviani
Abstract: An apparatus for scrubbing an image may include a memory storing instructions; and a processor configured to execute the instructions to: receive an input image; input a preset public attribute to an encoder neural network; obtain a scrubbed feature from the input image based on the preset public attribute, via the encoder neural network; wherein the encoder neural network is trained based on an amount of information in the scrubbed feature about the input image, and an estimated public attribute estimated from the scrubbed feature.
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公开(公告)号:US20230154102A1
公开(公告)日:2023-05-18
申请号:US17984521
申请日:2022-11-10
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Abstract: The present disclosure provides methods, apparatuses, and computer-readable mediums for representing shapes with probabilistic directed distance fields. In some embodiments, a method includes obtaining a camera representation and a latent shape vector representation of a scene. The camera representation indicates position information and direction information of a view of the scene. The method further includes calculating, based on the latent shape vector representation of the scene, a visibility score and a depth for each ray of a plurality of rays emanating from a corresponding plurality of positions and directions. The plurality of positions and directions are determined from the camera representation of the scene. The method further includes generating renders of geometric information of the scene using the visibility score and the depth of the plurality of rays.
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公开(公告)号:US20210193187A1
公开(公告)日:2021-06-24
申请号:US16725609
申请日:2019-12-23
Applicant: SAMSUNG ELECTRONICS CO., LTD.
Inventor: Caleb PHILLIPS , Iqbal MOHOMED , Afsaneh FAZLY , Allan JEPSON
IPC: G11B27/19 , G06K9/00 , G06F16/735 , G06F16/783
Abstract: An apparatus for video searching, includes a memory storing instructions, and a processor configured to execute the instructions to split a video into scenes, obtain, from the scenes into which the video is split, one or more textual descriptors describing each of the scenes, encode the obtained one or more textual descriptors describing each of the scenes into a video scene vector of each of the scenes, encode a user query into a query vector having a same semantic representation as that of the video scene vector of each of the scenes into which the one or more textual descriptors describing each of the scenes are encoded, and identify whether the video scene vector of at least one among the scenes corresponds to the query vector into which the user query is encoded.
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