WO2023036157 - SELF-SUPERVISED SPATIOTEMPORAL REPRESENTATION LEARNING BY EXPLORING VIDEO CONTINUITY
National phase entry is expected:
Publication Number
WO/2023/036157
Publication Date
16.03.2023
International Application No.
PCT/CN2022/117408
International Filing Date
07.09.2022
Title **
[English]
SELF-SUPERVISED SPATIOTEMPORAL REPRESENTATION LEARNING BY EXPLORING VIDEO CONTINUITY
[French]
APPRENTISSAGE AUTO-SUPERVISÉ D'UNE REPRÉSENTATION SPATIO-TEMPORELLE PAR EXPLORATION DE LA CONTINUITÉ VIDÉO
Applicants **
HUAWEI TECHNOLOGIES CO., LTD.
Inventors
LIANG, Hanwen
DAI, Peng
CHI, Zhixiang
CHEN, Lizhe
LU, Juwei
Priority Data
17/468,224
07.09.2021
US
Application details
| Total Number of Claims/PCT | * |
| Number of Independent Claims | * |
| Number of Priorities | * |
| Number of Multi-Dependent Claims | * |
| Number of Drawings | * |
| Pages for Publication | * |
| Number of Pages with Drawings | * |
| Pages of Specification | * |
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| Number of Office Actions | * |
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International Searching Authority |
CNIPA
* |
| Recordal of a Change of the Applicant's Name/Address |
Change of Applicant's Name and Address
* |
| Type of Assignment |
The Standard Agent's Assignment
* |
| Applicant's Legal Status |
Legal Entity
* |
| * | |
| * | |
| * | |
| * | |
| * | |
| Entry into National Phase under |
Chapter I
* |
| Patent Delivery |
Send the Letters Patent by Courier
* |
| Translation |
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* The data is based on automatic recognition. Please verify and amend if necessary.
** IP-Coster compiles data from publicly available sources. If this data includes your personal information, you can contact us to request its removal.
Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 2135 | |
| EPO | Filing, Examination, Granting | 14782 | |
| Japan | Filing, Examination, Granting | 2420 | |
| South Korea | Filing, Examination, Granting | 2554 | |
| USA | Filing, Examination, Granting | 4740 |
Total:
26,631
The term for entry into the National Phase has expired. This quotation is for informational purposes only
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Abstract[English]
A training method and apparatus are provided. The method includes feeding a primary video segment, representative of a concatenation of a first and a second nonadjacent video segments obtained from a video source, to a deep learning backbone network. The method further includes embedding, via the deep learning backbone network, the primary video segment into a first feature output. The method further includes providing the first feature output to a first perception network to generate a first set of probability distribution outputs indicating a temporal location of a discontinuous point associated with the primary video segment. The method further includes generating a first loss function based on the first set of probability distribution outputs. The method further includes optimizing the deep learning backbone network, by backpropagation of the first loss function.[French]
L'invention concerne un procédé et un appareil de formation. Le procédé consiste : à introduire un segment vidéo primaire, représentant une concaténation d'un premier et d'un second segment vidéo non adjacents obtenus à partir d'une source vidéo, dans un réseau fédérateur d'apprentissage profond ; à intégrer, par l'intermédiaire du réseau fédérateur d'apprentissage profond, le segment vidéo primaire dans une première sortie de caractéristiques ; à fournir la première sortie de caractéristiques à un premier réseau de perception pour générer un premier ensemble de sorties de distribution de probabilités indiquant un emplacement temporel d'un point discontinu associé au segment vidéo primaire ; à générer une première fonction de perte basée sur le premier ensemble de sorties de distribution de probabilités ; à optimiser le réseau fédérateur d'apprentissage profond, par rétropropagation de la première fonction de perte.