國立虎尾科技大學 |

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computer Vision – ECCV 2022/ edited by Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner.
Reminder of title:	17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX /
other author:	Avidan, Shai.
Description:	LV, 745 p. 222 illus., 220 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer vision. -
Online resource:	https://doi.org/10.1007/978-3-031-20056-4
ISBN:	9783031200564

Computer Vision – ECCV 2022 = 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX /
Computer Vision – ECCV 202217th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX /[electronic resource] :edited by Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner. - 1st ed. 2022. - LV, 745 p. 222 illus., 220 illus. in color.online resource. - Lecture Notes in Computer Science,136901611-3349 ;. - Lecture Notes in Computer Science,9324.

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.

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.

ISBN: 9783031200564

Standard No.: 10.1007/978-3-031-20056-4doiSubjects--Topical Terms:

561800
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Computer Vision – ECCV 2022 = 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX /
LDR:05412nam a22004095i 4500 001 1085119
003 DE-He213
005 20221102143342.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783031200564 $9 978-3-031-20056-4
024 7 $a 10.1007/978-3-031-20056-4 $2 doi
035 $a 978-3-031-20056-4
050 4 $a TA1634
072 7 $a UYQV $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYQV $2 thema
082 0 4 $a 006.37 $2 23
245 1 0 $a Computer Vision – ECCV 2022 $h [electronic resource] : $b 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX / $c edited by Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2022.
300 $a LV, 745 p. 222 illus., 220 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Lecture Notes in Computer Science, $x 1611-3349 ; $v 13690
505 0 $a 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.
520 $a 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.
650 0 $a Computer vision. $3 561800
650 0 $a Education—Data processing. $3 1253610
650 0 $a Computer engineering. $3 569006
650 0 $a Computer networks . $3 1365720
650 0 $a Social sciences—Data processing. $3 1280453
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computer Vision. $3 1127422
650 2 4 $a Computers and Education. $3 669806
650 2 4 $a Computer Engineering and Networks. $3 1365733
650 2 4 $a Computer Application in Social and Behavioral Sciences. $3 1365738
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Avidan, Shai. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1390727
700 1 $a Brostow, Gabriel. $e editor. $1 https://orcid.org/0000-0001-8472-3828 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1390728
700 1 $a Cissé, Moustapha. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1390729
700 1 $a Farinella, Giovanni Maria. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1022231
700 1 $a Hassner, Tal. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1102715
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783031200557
776 0 8 $i Printed edition: $z 9783031200571
830 0 $a Lecture Notes in Computer Science, $x 0302-9743 ; $v 9324 $3 1258960
856 4 0 $u https://doi.org/10.1007/978-3-031-20056-4
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)