作者单位:旷视科技(孙剑等人) 论文:Self-EMD: Self-Supervised Object Detection without ImageNet 在本文中,我们提出了一种新颖的用于目标检测的自监督表示学习方法Self-EMD。 我们的方法直接在未标记的non-iconic 图像数据集(例如COCO)上训练,而不是在常用的iconic-object 图像数据集(例如ImageNet)上训练。 我们将...
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...
Even with a batch size of one, SSLAD generates weights that are clearly superior to a random initialization while greatly outperforming other self-supervised methods. This property of SSLAD even allows for single-GPU training with only a minor decrease in performance....
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 ...
几篇论文实现代码:《Multi-task Self-supervised Object Detection via Recycling of Bounding Box Annotations》 (CVPR 2019) GitHub:http://t.cn/ESrPvLh 《Binary Image Selection (BISON): Interpretable E...
Physics-aware Self-supervised Training of CNNs for Object Detection Impressive progress has been achieved recently in object detection with the use of deep learning. Nevertheless, such tools typically require large amounts of training data and significant manual effort for labeling objects. This limits...
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...
[ECCV 2020] Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance depth-estimationdepth-predictionmultitask-learningself-supervisionmonocular-depth-estimationsgdepthdepth-from-single-images UpdatedMar 1, 2021 ...
【摘要】 论文分享:自监督综述Self-supervised Visual Feature Learning Deep Neural Networks: A Survey原文链接:https://arxiv.org/abs/1902.061621.1 自监督学习的意义 监督学习需要大量的标注数据来获得更好的表现,然而大量的标注数据的获取成本是极高的,自监督学习可以避免这个花销。1.2 术语定义 ... ...