Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks阅读笔记
Abstract： 挑战：（1）人脸不同的姿态（2）光照（3）遮挡。 本文研究：提出了一种深度级联的多任务框架，利用框架之间的内在相关性来提高它们的性能。 框架：级联结构——三层深度卷积网络，以粗到细的方式预测人脸和地标位置。 策略：在线困难样本挖掘策略——自动提高算法的性能而不需要人工样本筛选。 优势：在人脸检测方面：以FDDB和WIDER FACE为基准。 在人脸对齐方面：以AFLW为基
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相关文章
- 1. 论文笔记1.3——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 2. 论文笔记1.2——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 3. 论文笔记1——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 4. 论文笔记：Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 5. 论文：Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks 阅读笔记
- 6. MTCNN解读：Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 7. 论文解读——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks（二）
- 8. 论文解读——Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks（一）
- 9. 论文Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
- 10. Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks论文理解