rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Computer Vision - ECCV 2022
    sub title: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XIX
  name:
    name part: Avidan, Shai.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Brostow, Gabriel.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Cissé, Moustapha.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Farinella, Giovanni Maria.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Hassner, Tal.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2022
    edition: 1st ed. 2022.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (LVI, 761 pages 305 illustrations, 300 illustrations in color)
  abstract: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23-27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
  table of contents: Learning Mutual Modulation for Self-Supervised Cross-Modal Super Resolution -- Spectrum-Aware and Transferable Architecture Search for Hyperspectral Image Restoration -- Neural Color Operators for Sequential Image Retouching -- Optimizing Image Compression via Joint Learning with Denoising -- Restore Globally, Refine Locally: A Mask-Guided Scheme to Accelerate Super-Resolution Networks -- Compiler-Aware Neural Architecture Search for On-Mobile Real-Time Super-Resolution -- Modeling Mask Uncertainty in Hyperspectral Image Reconstruction -- Perceiving and Modeling Density for Image Dehazing -- Stripformer: Strip Transformer for Fast Image Deblurring -- Deep Fourier-Based Exposure Correction Network with Spatial-Frequency Interaction -- Frequency and Spatial Dual Guidance for Image Dehazing -- Towards Real-World HDRTV Reconstruction: A Data Synthesis Based Approach -- Learning Discriminative Shrinkage Deep Networks for Image Deconvolution -- KXNet: A Model-Driven Deep Neural Network for Blind Super Resolution -- ARM: Any-Time Super-Resolution Method -- Attention-Aware Learning for Hyperparameter Prediction in Image Processing Pipelines -- RealFlow: EM-Based Realistic Optical Flow Dataset Generation from Videos -- Memory-Augmented Model-Driven Network for Pansharpening -- All You Need Is RAW: Defending against Adversarial Attacks with Camera Image Pipelines -- Ghost-Free High Dynamic Range Imaging with Context-Aware Transformer -- Style-Guided Shadow Removal -- D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution -- GRIT-VLP: Grouped Mini-Batch Sampling for Efficient Vision and Language Pre-training -- Efficient Video Deblurring Guided by Motion Magnitude -- Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and a New Physics-Inspired Transformer Model -- Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression -- Image Super-Resolution with Deep Dictionary -- TempFormer: Te…
  note: edited by Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner.
  subject:
    topic: Computer vision
  subject:
    topic: Computer Vision
  classification: TA1634
  classification: 006.37
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Lecture Notes in Computer Science, 13679
  identifier: 9978-3-031-19800-7
  identifier: 9783031198007
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 221108
    record change date: 20240805183143.3
    record identifier: in00004659320