# A Fast Partial Video Copy Detection Using KNN and Global Feature Database

arXiv: https://arxiv.org/abs/2105.01713 · HTML: https://mlsanity.com/paper/2105.01713/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 4 May 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Video Action Transformer Network](https://mlsanity.com/paper/1812.02707/) (2018)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning](https://mlsanity.com/paper/1908.07410/) (2019)
- [Video Re-localization](https://mlsanity.com/paper/1808.01575/) (2018)
- [PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks](https://mlsanity.com/paper/2005.02154/) (2020)

## Built on by (most cited first, 4 of 4)

- [Video Similarity and Alignment Learning on Partial Video Copy Detection](https://mlsanity.com/paper/2108.01817/) (2021)
- [A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection](https://mlsanity.com/paper/2203.02654/) (2022)
- [VADER: Video Alignment Differencing and Retrieval](https://mlsanity.com/paper/2303.13193/) (2023)
- [Sync from the Sea: Retrieving Alignable Videos from Large-Scale Datasets](https://mlsanity.com/paper/2409.01445/) (2024)

## Abstract (arXiv)

We propose a fast partial video copy detection framework in this paper. In this framework all frame features of the reference videos are organized in a KNN searchable database. Instead of scanning all reference videos, the query video segment does a fast KNN search in the global feature database. The returned results are used to generate a short list of candidate videos. A modified temporal network is then used to localize the copy segment in the candidate videos. We evaluate different choice of CNN features on the VCDB dataset. Our benchmark F1 score exceeds the state of the art by a big margin.