self-supervised methods的目标是从unlabeled data中学习可以较好地迁移到下游任务的representation INTRODUCTION RELATED SURVEYS BACKGROUND ON OBJECT DETECTION FEW-SHOT OBJECT DETECTION SELF-SUPERVISED PRETRAINING TAKEAWAYS & TRENDS RELATED TASKS CONCLUSION 1★FEW-SHOT OBJECT DETECTION 定义:novel classes中每个class...
Self-supervised learningMasked autoencodersContrastive learningAutonomous drivingLiDAR-based 3D object detection is a crucial task for autonomous driving, owing to its accurate object recognition and localization capabilities in the 3D real-world space. However, existing methods heavily rely on time-...
论文名称:Self-EMD: Self-Supervised Object Detection without ImageNet 论文地址:https://arxiv.org/abs/2011.13677 核心思想 提出了一个应用于目标检测的自监督表示学习方法——self-EMD,可以直接采用COCO数据集(non-iconic)进行训练,不像传统的方法在ImageNet数据集(iconic-object)上进行训练。利用卷积特征图作...
In this paper, we propose a novel self-supervised representation learning method, Self-EMD, for object detection. Our method directly trained on unlabeled non-iconic image dataset like COCO, instead of commonly used iconic-object image dataset like ImageNet. We keep the convolutional feature maps ...
第三种Evaluation 就是将 Pretrained Model 运用在各种Vision Task 上,例如拿到 Object Detection 或 Segmentation 任务上依旧能表现不错。 回到CPC 这篇文章,ResNet-50 Linear Protocol 能达到 Top-1 71.5% 的准确率;在 Efficient Classification Protocol上,能比原本 Supervised Learning 的方式省掉至少 50% ~ 80...
第三种Evaluation 就是将 Pretrained Model 运用在各种Vision Task 上,例如拿到 Object Detection 或 Segmentation 任务上依旧能表现不错。 回到CPC 这篇文章,ResNet-50 Linear Protocol 能达到 Top-1 71.5% 的准确率;在 Efficient Classification Protocol上,能比原本 Supervised Learning 的方式省掉至少 50% ~ 80...
1. Self Paced Deep Learning for Weakly Supervised Object Detection(基于Self Paced深度学习的弱监督目标检测) 作者:Enver Sangineto,Moin Nabi,Dubravko Culibrk,Nicu Sebe 摘要:In a weakly-supervised scenario object detectors need to be trained using image-level annotation alone. Since bounding-box-level...
Through extensive experiments, we make a direct comparison between supervised and self-supervised learning on four datasets from three different domains (household, manufacturing and medical). For object classes seen during training, self-supervised and supervised learning are competitive. For unseen classe...
摘要: The construction of appearance-based object detection systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled in order to capture variations in object appearance. Semi-supervised training is a means for reducing...
02/21/2022 Initial commit: Code of TokenCut is released, including evaluation of unsupervised object discovery, unsupervised saliency object detection, weakly supervised object locolization. 2. Installation 2.1 Dependencies This code was implemented with Python 3.7, PyTorch 1.7.1 and CUDA 11.2. Please...