目标检测 - Generalized Focal Loss 基于one-stage检测器无cost涨点 (改进的Focal Loss,优于RetinaNet,FCOS,ATSS等) 目标检测 - Generalized Focal Loss 基于one-stage检测器无cost涨点 (改进的Focal Loss,优于RetinaNet,FCOS,ATSS等)blog.csdn.net/flyfish1986/article/details/110143467 李翔:大白话 Generaliz...
我们成功地解决了这个问题,将FL从{1,0}离散版本扩展到其连续变体,称为Generalized Focal Loss广义焦点损耗(GFL)。 Quality Focal Loss (QFL)专注于一组稀疏的难例子,同时对相应类别产生连续的0 ~ 1质量估计;Distribution Focal Loss(DFL)使得网络在任意、灵活分布的情况下,快速地专注于学习目标包围盒连续位置周...
前言:GFLv1文章名:Generalized Focal Loss Learning Qualified and Distributed Bounding Boxes for Dense Object Detection。GFLv1已经很漂亮了,没想到GFLv2能利用分布的统计特征来进行定位质量估计,而且还取得优于卷积特征的效果,感觉作者太强了。 (二)Summary 目前Localization Quality Estimation(LQE)在目标检测中是很...
深度学习论文: Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection及其PyTorch实现 PDF: https://arxiv.org/pdf/2011.12885.pdf PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks PyTorch: https://...
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection, CVPR2021 Resources Readme License Apache-2.0 license Activity Stars 0 stars Watchers 0 watching Forks 0 forks Report repository Releases No releases published Packages No packages published...
@article{li2020generalizedv2, title={Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection}, author={Li, Xiang and Wang, Wenhai and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian}, journal={arXiv preprint}, year={2020} } ...
we compute the language-image contrastive loss as [64]. We take the last valid token feature of Qt from the text encoder to represent a text as qˆ t and take the last entry in Os derived from X-Decoder as oˆ s . As a result, we obtain B pairs of features hqˆ t i , ...
Generalized Focal Loss V2 这篇文章提出了利用边界框分布(在Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection中提出的一般分布) 本文观察到一般的统计分布与其真正的定位质量有很强的相关性,边界框分布的形状(平整度)可以清楚地反... ...
源码和预训练模型地址:https://github.com/implus/GFocalV2 老规矩,还是一句话总结: 本文应该是检测领域首次引入用边界框的不确定性的统计量来高效地指导定位质量估计,从而基本无cost(包括在训练和测试阶段)地提升one-stage的检测器性能,涨幅在1~2个点AP。又是一个超超超良心技术,欢迎各位看官试用~ ...
HALF 深入理解一下Generalized Focal Loss v1 & v2 Mr.Joyi 解读Generalized Focal Loss Mario 论文阅读笔记:A Generalized Loss Function for Crowd Counting and Localization Hibari打开知乎App 在「我的页」右上角打开扫一扫 其他扫码方式:微信 下载知乎App 开通机构号 无障碍模式 验证码登录 密码登录 中国+86 获...