Computer Vision – ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIX / edited by Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner.

Format:

Book

Author/Creator:

Avidan, Shai, author.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 13679

Language:

English

Subjects (All):

Computer vision.

Computer Vision.

Local Subjects:

Computer Vision.

Physical Description:

1 online resource (817 pages)

Edition:

1st ed. 2022.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2022.

Summary:

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.

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: Temporally Consistent Transformer for Video Denoising

RAWtoBit: A Fully End-to-End Camera ISP Network

DRCNet: Dynamic Image Restoration Contrastive Network

Zero-Shot Learning for Reflection Removal of Single 360-Degree Image

Transformer with Implicit Edges for Particle-Based Physics Simulation

Rethinking Video Rain Streak Removal: A New Synthesis Model and a Deraining Network with Video Rain Prior

Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images

Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance

AlphaVC: High-Performance and Efficient Learned Video Compression

Content-Oriented Learned Image Compression

RRSR:Reciprocal Reference-Based Image Super-Resolution with Progressive Feature Alignment and Selection

Contrastive Prototypical Network with Wasserstein Confidence Penalty

Learn to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition

Self-Support Few-Shot Semantic Segmentation

Few-Shot Object Detection with Model Calibration

Self-Supervision Can Be a Good Few-Shot Learner.

Other Format:

Print version: Avidan, Shai Computer Vision - ECCV 2022

ISBN:

9783031198007

303119800X

OCLC:

1350685947