题目:Face Transformer for Recognition 作者:zhong et al.北邮 发表处:2021,没找到 代码:github.com/zhongyy/Face 论文贡献: 证明了变压器模型在人脸识别中的可行性 网络框架: 损失: 利用窗口滑动,生成可重叠的patch(别的论文也有用) 生成token 输入到transformer(用的是VIT的结构,未做改变) 人脸分类 实验: 参...
This is the code of Face Transformer for Recognition -LINK, forked fromzhongyy/Face-Transformer. Recently there has been great interests of Transformer not only inNLPbut also inComputer Vision (CV). We wonder if transformer can be used in face recognition and whether it is better thanCNNs. ...
Based on GDTA and CNNs, a novel, efficient, and lightweight face recognition model called CFormerFaceNet, which combines a CNN and Transformer, is proposed. The model significantly reduces the parameters and computational cost without compromising performance, greatly improving the compu...
采用了新一代的 Transformer 人脸表征模型 TransFace 后，FaceChain 去年也是推出了 10s 直接推理的人物写真极速生成工作，FaceChain-FACT。继 TransFace 之后，FaceChain 团队最近被机器学习顶级国际会议 NeurIPS 2024 接收了一篇人脸表征学习新作， "TopoFR: A Closer Look at Topology Alignment on Face Recognition"...
简介:本文介绍 阿里云开放视觉智能团队 被计算机视觉顶级国际会议ICCV 2023接收的论文 "TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective"。TransFace旨在探索ViT在人脸识别任务上表现不佳的原因,并从data-centric的角度去提升ViT在人脸识别任务上的性能。
If you like TransFace, please give us a star ⭐ on GitHub for the latest update~ This is the official PyTorch implementation of[ICCV-2023] TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric Perspective. ...
▌Multi-scale multi-modal micro-expression recognition algorithm 论文链接:https://arxiv.org/abs/2301.02969 transformer 网络 + 多模态、多尺度学习,微表情识别。 微表情是一种自发的无意识的面部肌肉运动,可以揭示人们试图隐藏的真实情绪。...
▌Multi-scale multi-modal micro-expression recognition algorithm 论文链接:https://arxiv.org/abs/2301.02969 transformer 网络 + 多模态、多尺度学习,微表情识别。 微表情是一种自发的无意识的面部肌肉运动,可以揭示人们试图隐藏的真实情绪。尽管人工方法已经取得了良好的进展,而深度学习的地位也越来越突出。由于微...
face recognition[翻译][深度学习理解人脸] 本文译自《Deep learning for understanding faces: Machines may be just as good, or better, than humans》。为了方便,文中论文索引位置保持不变,方便直接去原文中找参考文献。 近些年深度卷积神经网络的发展将各种目标检测和识别问题大大的向前推进了不少。这同时也得益...
Moreover, a convolutional self-attention network including the shallow feature extraction module (SFEM) and the convolution-Transformer dual-branch module (CTDM) is proposed to extract local- and global-range features for MFR. Experimental results on multiple public datasets demonstrate that our method...