Computer Vision – ECCV 2022 : 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part IX / 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 ; 13669

Language:

English

Subjects (All):

Computer vision.

Computer Vision.

Local Subjects:

Computer Vision.

Physical Description:

1 online resource (811 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:

BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

Category-Level 6D Object Pose and Size Estimation Using Self-Supervised Deep Prior Deformation Networks

Dense Teacher: Dense Pseudo-Labels for Semi-Supervised Object Detection

Point-to-Box Network for Accurate Object Detection via Single Point Supervision

Domain Adaptive Hand Keypoint and Pixel Localization in the Wild

Towards Data-Efficient Detection Transformers

Open-Vocabulary DETR with Conditional Matching

Prediction-Guided Distillation for Dense Object Detection

Multimodal Object Detection via Probabilistic Ensembling

Exploiting Unlabeled Data with Vision and Language Models for Object Detection

CPO: Change Robust Panorama to Point Cloud Localization

INT: Towards Infinite-Frames 3D Detection with an Efficient Framework

End-to-End Weakly Supervised Object Detection with Sparse Proposal Evolution

Calibration-Free Multi-View Crowd Counting

Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training

SuperLine3D: Self-Supervised Line Segmentation and Description for LiDAR Point Cloud

Exploring Plain Vision Transformer Backbones for Object Detection

Adversarially-Aware Robust Object Detector

HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors

You Should Look at All Objects

Detecting Twenty-Thousand Classes Using Image-Level Supervision

DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation

Monocular 3D Object Detection with Depth from Motion

DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation

Distilling Object Detectors with Global Knowledge

Unifying Visual Perception by Dispersible Points Learning

PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection

Exploring Resolution and Degradation Clues As Self-Supervised Signal for Low Quality Object Detection

Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features

Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection

RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection

Rethinking IoU-Based Optimization for Single-Stage 3D Object Detection

TD-Road: Top-Down Road Network Extraction with Holistic Graph Construction

Multi-faceted Distillation of Base-Novel Commonality for Few-Shot Object Detection

PointCLM: A Contrastive Learning-Based Framework for Multi-Instance Point Cloud Registration

Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration

MTTrans: Cross-Domain Object Detection with Mean Teacher Transformer

Multi-Domain Multi-Definition Landmark Localization for Small Datasets

DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection

Label-Guided Auxiliary Training Improves 3D Object Detector

PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images

Densely Constrained Depth Estimator for Monocular 3D Object Detection

Polarimetric Pose Prediction.

Other Format:

Print version: Avidan, Shai Computer Vision - ECCV 2022

ISBN:

9783031200779

3031200772