Computer Vision – ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX / 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 ; 13690

Language:

English

Subjects (All):

Computer vision.

Education--Data processing.

Education.

Computer engineering.

Computer networks.

Social sciences--Data processing.

Social sciences.

Artificial intelligence.

Computer Vision.

Computers and Education.

Computer Engineering and Networks.

Computer Application in Social and Behavioral Sciences.

Artificial Intelligence.

Local Subjects:

Computer Vision.

Computers and Education.

Computer Engineering and Networks.

Computer Application in Social and Behavioral Sciences.

Artificial Intelligence.

Physical Description:

1 online resource (801 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:

Fast Two-View Motion Segmentation Using Christoffel Polynomials

UCTNet: Uncertainty-Aware Cross-Modal Transformer Network for Indoor RGB-D Semantic Segmentation

Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation

Learning Regional Purity for Instance Segmentation on 3D Point Clouds

Cross-Domain Few-Shot Semantic Segmentation

Generative Subgraph Contrast for Self-Supervised Graph Representation Learning

SdAE: Self-Distillated Masked Autoencoder

Demystifying Unsupervised Semantic Correspondence Estimation

Open-Set Semi-Supervised Object Detection

Vibration-Based Uncertainty Estimation for Learning from Limited Supervision

Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation

Weakly Supervised Object Localization through Inter-class Feature Similarity and Intra-Class Appearance Consistency

Active Learning Strategies for Weakly-Supervised Object Detection

Mc-BEiT: Multi-Choice Discretization for Image BERT Pre-training

Bootstrapped Masked Autoencoders for Vision BERT Pretraining

Unsupervised Visual Representation Learning by Synchronous Momentum Grouping

Improving Few-Shot Part Segmentation Using Coarse Supervision

What to Hide from Your Students: Attention-Guided Masked Image Modeling

Pointly-Supervised Panoptic Segmentation

MVP: Multimodality-Guided Visual Pre-training

Locally Varying Distance Transform for Unsupervised Visual Anomaly Detection

HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

Dual-Domain Self-Supervised Learning and Model Adaption for Deep Compressive Imaging

Unsupervised Selective Labeling for More Effective Semi-Supervised Learning

Max Pooling with Vision Transformers Reconciles Class and Shape in Weakly Supervised Semantic Segmentation

Dense Siamese Network for Dense Unsupervised Learning

Multi-Granularity Distillation Scheme towards Lightweight Semi-Supervised Semantic Segmentation

CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation

Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization

RDA: Reciprocal Distribution Alignment for Robust Semi-Supervised Learning

MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation

United Defocus Blur Detection and Deblurring via Adversarial Promoting Learning

Synergistic Self-Supervised and Quantization Learning

Semi-Supervised Vision Transformers

Domain Adaptive Video Segmentation via Temporal Pseudo Supervision

Diverse Learner: Exploring Diverse Supervision for Semi-Supervised Object Detection

A Closer Look at Invariances in Self-Supervised Pre-training for 3D Vision

ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization

FedX: Unsupervised Federated Learning with Cross Knowledge Distillation

W2N: Switching from Weak Supervision to Noisy Supervision for Object Detection

Decoupled Adversarial Contrastive Learning for Self-Supervised Adversarial Robustness.

Other Format:

Print version: Avidan, Shai Computer Vision - ECCV 2022

ISBN:

9783031200564

303120056X