人脸检测论文英语
人脸检测论文英语
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Google出品。亚毫秒级的移动端人脸检测算法。移动端可达200~1000+FPS速度。主要以下改进:
在深度可分离卷积中,计算量主要为point-wise部分,增加depth-wise部分卷积核大小并不会明显增加成本。因此本文在depth-wise部分采用了5x5的卷积核,已获得更大的感受野,故此可以降低在层数上的需求。
此外,启发于mobilenetV2,本文设计了一个先升后降的double BlazeBlock。BlazeBlock适用于浅层,double BlazeBlock适用于深层。
16x16的anchor是一样的,但本文将8x8,4x4和2x2的2个anchor替换到8x8的6个anchor。此外强制限制人脸的长宽为1:1。
由于最后一层feature map较大(相对于ssd),导致预测结果会较多,在连续帧预测过程中,nms会变导致人脸框变得更加抖动。本文在原始边界框的回归参数估计变为其与重叠概率的加权平均。这基本没有带来预测时间上的消耗,但在提升了10%的性能。
效果好速度快的方法想不想要?
人脸造假检测论文(五)
姓名:张钰 学号:21011210154 学院:通信工程学院
【嵌牛导读】Representative Forgery Mining for Fake Face Detection论文阅读笔记
【嵌牛鼻子】北邮提出的RFM 框架,可以在没有精心设计的监督情况下将显著的伪造行为可视化,并使基于通用CNN 的检测器在DFFD 和 Celeb-DF 上实现 SOTA 性能。
【嵌牛提问】如何实现伪造检测,有何创新点
【嵌牛正文】
转自:
该方法通过将检测器的注意力从过度敏感的面部区域解耦,实现了具有先进水平的检测性能,并显著保持了对仅包含少量技术伪造的人脸的检测性能。
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请高手帮我翻译下面文字(中译英)
An examination of person is an initial link for identify, the problem that it handle is to confirm the picture( or image) to win whether exist person's face, if the existence then carries on the fixed position to person's applied realm of an examination of person is very extensive, is one of the important steps that carries out the machine intelligence to turn.
The calculate way of AdaBoost is an examination of a kind of fast person calculate way put forward in 1995, is the progress of an examination of person realm milestone type, this kind of calculate way according to the feedback of the weak study, the adaptability ground adjusted the supposed mistake rate, make under the condition that efficiency does not lower, examined the correctness to get the very big exaltation.
The This thesis the usage Haar the characteristic sets the up the the weak classification machine, the the usage the classic Adaboost the method trained the the strong the classification machine, the the system the related the the theories knowledge the of the the calculate way the of Adaboost, the and at the use the the small scale the typical model the data to the experimented to the compare the the result of the the procedure up, pass to compare time to consume, the memory take up, declining the comparison of 维 , identifying the mistake rate etc., basic carried out the classification function of the machine.
人脸造假检测论文(二)
姓名:张钰 学号:21011210154 学院:通信工程学院
【嵌牛导读】Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection论文阅读笔记
【嵌牛鼻子】Deepfake人脸检测方法,基于单中心损失监督的频率感知鉴别特征学习框架FDFL,将度量学习和自适应频率特征学习应用于人脸伪造检测,实现SOTA性能
【嵌牛提问】本文对于伪造人脸检测的优势在哪里体现
【嵌牛正文】
转自:
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