Existing video copy detection methods generally measure video similarity
based on spatial similarities between key frames, neglecting the latent
similarity in temporal dimension, so that the video similarity is biased
towards spatial information. There are methods modeling unified video
similarity in an end-to-end way, but losing detailed partial alignment
information, which causes the incapability of copy segments localization. To
address the above issues, we propose the Video Similarity and Alignment
Learning (VSAL) approach, which jointly models spatial similarity, temporal
similarity and partial alignment. To mitigate the spatial similarity bias, we
model the temporal similarity as the mask map predicted from frame-level
spatial similarity, where each element indicates the probability of frame pair
lying right on the partial alignments. To further localize partial copies, the
step map is learned from the spatial similarity where the elements indicate
extending directions of the current partial alignments on the spatial-temporal
similarity map. Obtained from the mask map, the start points extend out into
partial optimal alignments following instructions of the step map. With the
similarity and alignment learning strategy, VSAL achieves the state-of-the-art
F1-score on VCDB core dataset. Furthermore, we construct a new benchmark of
partial video copy detection and localization by adding new segment-level
annotations for FIVR-200k dataset, where VSAL also achieves the best
performance, verifying its effectiveness in more challenging situations. Our
project is publicly available at this https URL

提出了 Video Similarity and Alignment Learning（VSAL）方法，通过模拟时空相似性和部分对齐来实现视频相似性学习，从而提高视频复制检测的准确率。通过在 VCDB 核心数据集上测试，证明 VSAL 的 F1 得分高于现有模型，并通过在 FIVR-200k 数据集上添加新的片段级注释来验证在更具挑战性的情况下的有效性。

针对部分视频复制检测的视频相似度和对齐学习

Video Similarity and Alignment Learning on Partial Video Copy Detection

Given two graphs, the graph matching problem is to align the two vertex sets
so as to minimize the number of adjacency disagreements between the two graphs.
The seeded graph matching problem is the graph matching problem when we are
first given a partial alignment that we are tasked with completing. In this
paper, we modify the state-of-the-art approximate graph matching algorithm
"FAQ" of Vogelstein et al. (2015) to make it a fast approximate seeded graph
matching algorithm, adapt its applicability to include graphs with differently
sized vertex sets, and extend the algorithm so as to provide, for each
individual vertex, a nomination list of likely matches. We demonstrate the
effectiveness of our algorithm via simulation and real data experiments;
indeed, knowledge of even a few seeds can be extremely effective when our
seeded graph matching algorithm is used to recover a naturally existing
alignment that is only partially observed.

该论文提出了一种快速的基于图形的相似匹配算法，通过模拟和实际数据实验验证了该算法的有效性，能够从部分观察到的自然存在的对齐中恢复信息。