This repository contains the Tensorflow implementation of the paper ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning. It provides code for the calculation of similarities between the query and database videos given by the user. Also, it contains an evaluation script to reproduce the results of the paper.

2019年8月20日 · In this paper we introduce ViSiL, a Video Similarity Learning architecture that considers fine-grained Spatio-Temporal relations between pairs of videos -- such relations are typically lost in previous video retrieval approaches that embed the whole frame or even the whole video into a vector descriptor before the similarity estimation.

In this paper we introduce ViSiL, a Video Similarity Learning architecture that considers fine-grained Spatio-Temporal relations between pairs of videos – such relations are typically lost in previous video retrieval approaches that embed the whole frame or even the whole video into a vec-tor descriptor before the similarity estimation.

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2021年8月26日 · 根据visil论文，利用神经网络提取高级特征比较区域、帧及视频相似性，证明去冗余不影响精度。 背景对相似性分数影响较大，考虑前景提取在验证去冗余实验中。

ViSiL是一种基于深度学习的视频相似性学习技术，它可以在细粒度的空间和时间尺度上比较视频之间的相似性。 ViSiL的核心思想是使用卷积神经网络（CNN）来提取视频中每一帧的空间信息和每一帧之间的时序信息，并使用...

2019年10月1日 · In 2019, Giorgos et al. [24] have introduced a Video Similarity Learning (ViSiL) architecture that considers fine-grained spatio-temporal relations between pairs of videos.

2024年4月5日 · ViSiL是一种基于细粒度时空视频相似性学习的方法。 它主要用于视频检索、视频分类和视频推荐等应用。 ViSiL的核心思想是利用卷积神经网络（Convolutional Neural Networks，CNN）学习视频的时空特征，并使用双线性汇合（Bilinear Pooling）方法进行特征融合，从而提高视频的相似度计算精度。 ViSiL主要由两个模块组成：时空特征提取模块和相似度计算模块。 时空特征提取模块采用3D卷积神经网络对视频进行特征提取；相似度计算模块采用双 …

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