In this paper, we focus on the task of extracting visual correspondences
across videos. Given a query video clip from an action class, we aim to align
it with training videos in space and time. Obtaining training data for such a
fine-grained alignment task is challenging and often ambiguous. Hence, we
propose a novel alignment procedure that learns such correspondence in space
and time via cross video cycle-consistency. During training, given a pair of
videos, we compute cycles that connect patches in a given frame in the first
video by matching through frames in the second video. Cycles that connect
overlapping patches together are encouraged to score higher than cycles that
connect non-overlapping patches. Our experiments on the Penn Action and Pouring
datasets demonstrate that the proposed method can successfully learn to
correspond semantically similar patches across videos, and learns
representations that are sensitive to object and action states.

本文讨论了跨视频提取视觉对应关系的任务，提出了一种使用交叉视频循环一致性来学习空间和时间对应关系的新方法，成功实现了语义相似的贴片在视频中的对应，并学习到对物体和动作状态敏感的表示。

时空视频对齐

Aligning Videos in Space and Time

Convolutional Neural Networks (CNNs) have proven very effective in image
classification and show promise for audio. We use various CNN architectures to
classify the soundtracks of a dataset of 70M training videos (5.24 million
hours) with 30,871 video-level labels. We examine fully connected Deep Neural
Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We
investigate varying the size of both training set and label vocabulary, finding
that analogs of the CNNs used in image classification do well on our audio
classification task, and larger training and label sets help up to a point. A
model using embeddings from these classifiers does much better than raw
features on the Audio Set [5] Acoustic Event Detection (AED) classification
task.

本文介绍了使用几种不同的卷积神经网络对音频进行分类，发现在大型数据集上运用图像分类中的卷积神经网络架构对于音频分类也十分有效，使用嵌入分类器的模型对音频事件检测的任务表现优于对原始特征的直接使用。