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Computer Vision – ECCV 2022

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Cover of 'Computer Vision – ECCV 2022'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
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    Chapter 2 OSFormer: One-Stage Camouflaged Instance Segmentation with Transformers
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    Chapter 3 Highly Accurate Dichotomous Image Segmentation
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    Chapter 4 Boosting Supervised Dehazing Methods via Bi-level Patch Reweighting
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    Chapter 5 Flow-Guided Transformer for Video Inpainting
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    Chapter 6 Shift-Tolerant Perceptual Similarity Metric
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    Chapter 7 Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution
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    Chapter 8 VQFR: Blind Face Restoration with Vector-Quantized Dictionary and Parallel Decoder
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    Chapter 9 Uncertainty Learning in Kernel Estimation for Multi-stage Blind Image Super-Resolution
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    Chapter 10 Learning Spatio-Temporal Downsampling for Effective Video Upscaling
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    Chapter 11 Learning Local Implicit Fourier Representation for Image Warping
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    Chapter 12 SepLUT: Separable Image-Adaptive Lookup Tables for Real-Time Image Enhancement
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    Chapter 13 Blind Image Decomposition
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    Chapter 14 MuLUT: Cooperating Multiple Look-Up Tables for Efficient Image Super-Resolution
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    Chapter 15 Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution
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    Chapter 16 Spatial-Frequency Domain Information Integration for Pan-Sharpening
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    Chapter 17 Adaptive Patch Exiting for Scalable Single Image Super-Resolution
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    Chapter 18 Efficient Meta-Tuning for Content-Aware Neural Video Delivery
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    Chapter 19 Reference-Based Image Super-Resolution with Deformable Attention Transformer
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    Chapter 20 Local Color Distributions Prior for Image Enhancement
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    Chapter 21 L-CoDer: Language-Based Colorization with Color-Object Decoupling Transformer
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    Chapter 22 From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
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    Chapter 23 Towards Interpretable Video Super-Resolution via Alternating Optimization
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    Chapter 24 MEFNet: Multi-scale Event Fusion Network for Motion Deblurring
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    Chapter 25 Fast and High Quality Image Denoising via Malleable Convolution
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    Chapter 26 TAPE: Task-Agnostic Prior Embedding for Image Restoration
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    Chapter 27 Uncertainty Inspired Underwater Image Enhancement
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    Chapter 28 Hourglass Attention Network for Image Inpainting
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    Chapter 29 Unfolded Deep Kernel Estimation for Blind Image Super-Resolution
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    Chapter 30 Event-guided Deblurring of Unknown Exposure Time Videos
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    Chapter 31 ReCoNet: Recurrent Correction Network for Fast and Efficient Multi-modality Image Fusion
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    Chapter 32 Content Adaptive Latents and Decoder for Neural Image Compression
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    Chapter 33 Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
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    Chapter 34 Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-Ahead Forward Ones
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    Chapter 35 Self-supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
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    Chapter 36 Secrets of Event-Based Optical Flow
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    Chapter 37 Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoiréing
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    Chapter 38 ERDN: Equivalent Receptive Field Deformable Network for Video Deblurring
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    Chapter 39 Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion
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    Chapter 40 ART-SS: An Adaptive Rejection Technique for Semi-supervised Restoration for Adverse Weather-Affected Images
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    Chapter 41 Fusion from Decomposition: A Self-Supervised Decomposition Approach for Image Fusion
  43. Altmetric Badge
    Chapter 42 Learning Degradation Representations for Image Deblurring
Attention for Chapter 32: Content Adaptive Latents and Decoder for Neural Image Compression
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Chapter title
Content Adaptive Latents and Decoder for Neural Image Compression
Chapter number 32
Book title
Computer Vision – ECCV 2022
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-19797-0_32
Book ISBNs
978-3-03-119796-3, 978-3-03-119797-0
Authors

Pan, Guanbo, Lu, Guo, Hu, Zhihao, Xu, Dong

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 23%
Student > Master 2 15%
Lecturer 1 8%
Unknown 7 54%
Readers by discipline Count As %
Computer Science 5 38%
Unknown 8 62%