We conduct an in-depth exploration of different strategies for doing event
detection in videos using convolutional neural networks (CNNs) trained for
image classification. We study different ways of performing spatial and
temporal pooling, feature normalization, choice of CNN layers as well as choice
of classifiers. Making judicious choices along these dimensions led to a very
significant increase in performance over more naive approaches that have been
used till now. We evaluate our approach on the challenging TRECVID MED'14
dataset with two popular CNN architectures pretrained on ImageNet. On this
MED'14 dataset, our methods, based entirely on image-trained CNN features, can
outperform several state-of-the-art non-CNN models. Our proposed late fusion of
CNN- and motion-based features can further increase the mean average precision
(mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the
state-of-the-art classification performance on the challenging UCF-101 dataset.

本文探索了使用卷积神经网络（CNN）进行视频事件检测的不同策略，包括空间和时间池化、特征归一化、CNN 层的选择以及分类器的选择。通过在这些维度上进行合理选择，我们的方法在 TRECVID MED'14 数据集上的性能可比先前使用的非 - CNN 模型具有显著提高，并且我们的融合方法可以进一步提高平均精度（mAP）并达到最新的分类性能水平。

利用图像训练的 CNN 架构进行非约束视频分类

Exploiting Image-trained CNN Architectures for Unconstrained Video  Classification

In the past few years, a lot of attention has been devoted to multimedia
indexing by fusing multimodal informations. Two kinds of fusion schemes are
generally considered: The early fusion and the late fusion. We focus on late
classifier fusion, where one combines the scores of each modality at the
decision level. To tackle this problem, we investigate a recent and elegant
well-founded quadratic program named MinCq coming from the machine learning
PAC-Bayesian theory. MinCq looks for the weighted combination, over a set of
real-valued functions seen as voters, leading to the lowest misclassification
rate, while maximizing the voters' diversity. We propose an extension of MinCq
tailored to multimedia indexing. Our method is based on an order-preserving
pairwise loss adapted to ranking that allows us to improve Mean Averaged
Precision measure while taking into account the diversity of the voters that we
want to fuse. We provide evidence that this method is naturally adapted to late
fusion procedures and confirm the good behavior of our approach on the
challenging PASCAL VOC'07 benchmark.

研究了多媒体索引中的后期分类器融合问题，提出了一种基于 MinCq 的扩展方法，使用一个适用于排名的有序损失函数来提高平均准确率，同时考虑初始投票者的多样性。该方法在 PASCAL VOC'07 基准测试中表现良好。