Computer Vision – ECCV 2022

17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX

AvShai Avidan,Gabriel Brostow

Häftad, Engelska, 2022

Del 13690 i serien Lecture Notes in Computer Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-11-03
Mått:155 x 235 x 43 mm
Vikt:1 194 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:745
Förlag:Springer International Publishing AG
ISBN:9783031200557

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Innehållsförteckning

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-SupervisedObject 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.