Convolutional Architecture for Fast Feature Encoding (CAFFE) [11] is a
software package for the training, classifying, and feature extraction of
images. The UCF Sports Action dataset is a widely used machine learning dataset
that has 200 videos taken in 720x480 resolution of 9 different sporting
activities: diving, golf, swinging, kicking, lifting, horseback riding,
running, skateboarding, swinging (various gymnastics), and walking. In this
report we report on a caffe feature extraction pipeline of images taken from
the videos of the UCF Sports Action dataset. A similar test was performed on
overfeat, and results were inferior to caffe. This study is intended to explore
the architecture and hyper parameters needed for effective static analysis of
action in videos and classification over a variety of image datasets.

本文探索用于视频静态分析和图像分类的 Convolutional Architecture for Fast Feature Encoding (CAFFE) 软件包，并使用 UCF Sports Action 数据集的视频进行 caffes 特征提取流程。结果表明，caffe 在类似测试中的表现优于 overfeat。

用于动作识别和图像分类的卷积架构探索

Convolutional Architecture Exploration for Action Recognition and Image  Classification

This paper proposes a new deep convolutional neural network (DCNN)
architecture that learns pixel embeddings, such that pairwise distances between
the embeddings can be used to infer whether or not the pixels lie on the same
region. That is, for any two pixels on the same object, the embeddings are
trained to be similar; for any pair that straddles an object boundary, the
embeddings are trained to be dissimilar. Experimental results show that when
this embedding network is used in conjunction with a DCNN trained on semantic
segmentation, there is a systematic improvement in per-pixel classification
accuracy. Our contributions are integrated in the popular Caffe deep learning
framework, and consist in straightforward modifications to convolution
routines. As such, they can be exploited for any task involving convolution
layers.

本文提出了基于像素 embeddings 的深层卷积神经网络，利用嵌入学习像素之间的距离来推断像素是否属于同一区域，并证明其与 DCNN 结合使用可以显著提高每个像素分类的准确性。

学习密集卷积嵌入用于语义分割

Learning Dense Convolutional Embeddings for Semantic Segmentation

Caffe provides multimedia scientists and practitioners with a clean and
modifiable framework for state-of-the-art deep learning algorithms and a
collection of reference models. The framework is a BSD-licensed C++ library
with Python and MATLAB bindings for training and deploying general-purpose
convolutional neural networks and other deep models efficiently on commodity
architectures. Caffe fits industry and internet-scale media needs by CUDA GPU
computation, processing over 40 million images a day on a single K40 or Titan
GPU ($\approx$ 2.5 ms per image). By separating model representation from
actual implementation, Caffe allows experimentation and seamless switching
among platforms for ease of development and deployment from prototyping
machines to cloud environments. Caffe is maintained and developed by the
Berkeley Vision and Learning Center (BVLC) with the help of an active community
of contributors on GitHub. It powers ongoing research projects, large-scale
industrial applications, and startup prototypes in vision, speech, and
multimedia.

Caffe 是一个跨平台的深度学习框架，提供了用于训练和部署卷积神经网络及其他深度模型的 Python 和 MATLAB 接口，并通过 CUDA GPU 进行加速，每天可处理超过 4000 万张图像。由伯克利视觉与学习中心维护和开发，用于视觉、语音和多媒体的研究项目和大型产业应用。