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Computer Vision – ECCV 2022

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Table of Contents

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    Book Overview
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    Chapter 1 BEVFormer: Learning Bird’s-Eye-View Representation from Multi-camera Images via Spatiotemporal Transformers
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    Chapter 2 Category-Level 6D Object Pose and Size Estimation Using Self-supervised Deep Prior Deformation Networks
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    Chapter 3 Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
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    Chapter 4 Point-to-Box Network for Accurate Object Detection via Single Point Supervision
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    Chapter 5 Domain Adaptive Hand Keypoint and Pixel Localization in the Wild
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    Chapter 6 Towards Data-Efficient Detection Transformers
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    Chapter 7 Open-Vocabulary DETR with Conditional Matching
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    Chapter 8 Prediction-Guided Distillation for Dense Object Detection
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    Chapter 9 Multimodal Object Detection via Probabilistic Ensembling
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    Chapter 10 Exploiting Unlabeled Data with Vision and Language Models for Object Detection
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    Chapter 11 CPO: Change Robust Panorama to Point Cloud Localization
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    Chapter 12 INT: Towards Infinite-Frames 3D Detection with an Efficient Framework
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    Chapter 13 End-to-End Weakly Supervised Object Detection with Sparse Proposal Evolution
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    Chapter 14 Calibration-Free Multi-view Crowd Counting
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    Chapter 15 Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-training
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    Chapter 16 SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
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    Chapter 17 Exploring Plain Vision Transformer Backbones for Object Detection
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    Chapter 18 Adversarially-Aware Robust Object Detector
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    Chapter 19 HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors
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    Chapter 20 You Should Look at All Objects
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    Chapter 21 Detecting Twenty-Thousand Classes Using Image-Level Supervision
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    Chapter 22 DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation
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    Chapter 23 Monocular 3D Object Detection with Depth from Motion
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    Chapter 24 DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation
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    Chapter 25 Distilling Object Detectors with Global Knowledge
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    Chapter 26 Unifying Visual Perception by Dispersible Points Learning
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    Chapter 27 PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
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    Chapter 28 Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
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    Chapter 29 Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features
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    Chapter 30 Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection
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    Chapter 31 RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection
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    Chapter 32 Rethinking IoU-based Optimization for Single-stage 3D Object Detection
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    Chapter 33 TD-Road: Top-Down Road Network Extraction with Holistic Graph Construction
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    Chapter 34 Multi-faceted Distillation of Base-Novel Commonality for Few-Shot Object Detection
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    Chapter 35 PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration
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    Chapter 36 Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
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    Chapter 37 MTTrans: Cross-domain Object Detection with Mean Teacher Transformer
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    Chapter 38 Multi-domain Multi-definition Landmark Localization for Small Datasets
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    Chapter 39 DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection
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    Chapter 40 Label-Guided Auxiliary Training Improves 3D Object Detector
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    Chapter 41 PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images
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    Chapter 42 Densely Constrained Depth Estimator for Monocular 3D Object Detection
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    Chapter 43 Polarimetric Pose Prediction
Attention for Chapter 28: Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
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Chapter title
Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
Chapter number 28
Book title
Computer Vision – ECCV 2022
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-20077-9_28
Book ISBNs
978-3-03-120076-2, 978-3-03-120077-9
Authors

Cui, Ziteng, Zhu, Yingying, Gu, Lin, Qi, Guo-Jun, Li, Xiaoxiao, Zhang, Renrui, Zhang, Zenghui, Harada, Tatsuya

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 27%
Student > Master 5 23%
Student > Doctoral Student 1 5%
Other 1 5%
Researcher 1 5%
Other 0 0%
Unknown 8 36%
Readers by discipline Count As %
Computer Science 12 55%
Engineering 1 5%
Unknown 9 41%