最近读了Joint Training of Cascaded CNN for Face Detection这篇论文,论文中把之前人脸检测使用到的cascade cnn,从分开训练的模式,改为了联合训练,并且声称得到了更好的结果. 但是在我读论文的过程中,产生了下面几点疑惑: 1.论文4.2节的Training procedure这部分最后提到,To make it converge easily, we train seperate networks and initialize the joi…

# coding: utf-8 import tensorflow as tf class TCNNConfig(object): """CNN配置参数""" embedding_dim = 20 # 词向量维度 seq_length = 100 # 序列长度 num_classes = 73 # 类别数 num_filters = 256 # 卷积核数目 kernel_size = 5 # 卷积核尺寸 vocab_size = 5000 # 词…

http://www.cv-foundation.org/openaccess/CVPR2016.py ORAL SESSION Image Captioning and Question Answering Monday, June 27th, 9:00AM - 10:05AM. These papers will also be presented at the following poster session 1   Deep Compositional Captioning: Descr…

CVPR2016 Paper list ORAL SESSIONImage Captioning and Question Answering Monday, June 27th, 9:00AM - 10:05AM. These papers will also be presented at the following poster session 1 Deep Compositional Captioning: Describing Novel Object Categories Witho…

项目链接 Abstract 在该论文中,作者首先介绍了对YOLOv1检测系统的各种改进措施.改进后得到的模型被称为YOLOv2,它使用了一种新颖的多尺度训练方法,使得模型可以在不同尺寸的输入上运行,并在速度和精度上很容易找到平衡.当处理速度为40FPS时,YOLOv2取得76.8mAP的成绩,超过了当时最好的检测方法Faster RCNN with ResNet和SSD 接着,作者提出了一种在object detection和classification两个任务上进行联合训练的方法.借助该方法,…

一. 源起于Faster 深度学习于目标检测的里程碑成果,来自于这篇论文: Ren, Shaoqing, et al. "Faster R-CNN: Towards real-time object detection with region proposal networks." Advances in Neural Information Processing Systems. 2015. 也可以参考:[论文翻译] 虽然该文章前面已经讲过,但只给出了很小的篇幅,并没有作为独立的一篇…

Github上的一个开源项目,文档讲得极清晰 Github - https://github.com/dennybritz/cnn-text-classification-tf 原文- http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/ In this post we will implement a model similar to Kim Yoon’s Convolut…

https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/ https://blog.keras.io/building-autoencoders-in-keras.html https://www.kaggle.com/atom1231/keras-autoencoder-with-simple-cnn-kfold4-lb-1704 https://datasci…

本文来自<MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices>,时间线为2018年11月.是作者分别来自CMU和uark学校. 0 引言 随着DCNN的普及,在目标检测,目标分割等领域都有不小的进步,然而其较高准确度背后却是大量的参数和计算量.如AlexNet需要61百万参数量,VGG16需要138百万参数量,Resnet-50需要25百万参数量.Densenet190(k=40)需要40百万参数量.…

@http://www-cs-faculty.stanford.edu/people/karpathy/cvpr2015papers/ CVPR 2015 papers (in nicer format than this) maintained by @karpathy NEW: This year I also embedded the (1,2-gram) tfidf vectors of all papers with t-sne and placed them in an interf…