BR112023017706A2 - Adaptação eficiente a tempo de teste para consistência temporal aperfeiçoada em processamento de vídeo - Google Patents

Adaptação eficiente a tempo de teste para consistência temporal aperfeiçoada em processamento de vídeo

Info

Publication number
BR112023017706A2
BR112023017706A2 BR112023017706A BR112023017706A BR112023017706A2 BR 112023017706 A2 BR112023017706 A2 BR 112023017706A2 BR 112023017706 A BR112023017706 A BR 112023017706A BR 112023017706 A BR112023017706 A BR 112023017706A BR 112023017706 A2 BR112023017706 A2 BR 112023017706A2
Authority
BR
Brazil
Prior art keywords
video
video processing
adaptation
consistency
improved
Prior art date
Application number
BR112023017706A
Other languages
English (en)
Inventor
Murat Porikli Fatih
Mangesh Borse Shubhankar
Yizhe Zhang
Original Assignee
Qualcomm Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Qualcomm Inc filed Critical Qualcomm Inc
Publication of BR112023017706A2 publication Critical patent/BR112023017706A2/pt

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)
  • Television Systems (AREA)

Abstract

adaptação eficiente a tempo de teste para consistência temporal aperfeiçoada em processamento de vídeo. um método para processar um vídeo inclui receber um vídeo como uma entrada em uma primeira camada de uma rede neural artificial (ann). um primeiro quadro do vídeo é processado para gerar um primeiro rótulo. depois disso, a rede neural artificial é atualizada com base no primeiro rótulo. a atualização é desempenhada enquanto simultaneamente se está processando um segundo quadro do vídeo. ao fazer isso, a inconsistência temporal entre rótulos é reduzida.
BR112023017706A 2021-03-10 2022-03-09 Adaptação eficiente a tempo de teste para consistência temporal aperfeiçoada em processamento de vídeo BR112023017706A2 (pt)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US17/198,147 US20220292302A1 (en) 2021-03-10 2021-03-10 Efficient test-time adaptation for improved temporal consistency in video processing
PCT/US2022/019620 WO2022192449A1 (en) 2021-03-10 2022-03-09 Efficient test-time adaptation for improved temporal consistency in video processing

Publications (1)

Publication Number Publication Date
BR112023017706A2 true BR112023017706A2 (pt) 2023-12-12

Family

ID=81326760

Family Applications (1)

Application Number Title Priority Date Filing Date
BR112023017706A BR112023017706A2 (pt) 2021-03-10 2022-03-09 Adaptação eficiente a tempo de teste para consistência temporal aperfeiçoada em processamento de vídeo

Country Status (7)

Country Link
US (1) US20220292302A1 (pt)
EP (1) EP4305551A1 (pt)
JP (1) JP2024509862A (pt)
KR (1) KR20230154187A (pt)
CN (1) CN117223035A (pt)
BR (1) BR112023017706A2 (pt)
WO (1) WO2022192449A1 (pt)

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018128741A1 (en) * 2017-01-06 2018-07-12 Board Of Regents, The University Of Texas System Segmenting generic foreground objects in images and videos
EP3608844A1 (en) * 2018-08-10 2020-02-12 Naver Corporation Methods for training a crnn and for semantic segmentation of an inputted video using said crnn

Also Published As

Publication number Publication date
US20220292302A1 (en) 2022-09-15
JP2024509862A (ja) 2024-03-05
EP4305551A1 (en) 2024-01-17
WO2022192449A1 (en) 2022-09-15
KR20230154187A (ko) 2023-11-07
CN117223035A (zh) 2023-12-12

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