# FIVR: Fine-grained Incident Video Retrieval

arXiv: https://arxiv.org/abs/1809.04094 · HTML: https://mlsanity.com/paper/1809.04094/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 26 of 26)

- [The 2021 Image Similarity Dataset and Challenge](https://mlsanity.com/paper/2106.09672/) (2021)
- [DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval](https://mlsanity.com/paper/2106.13266/) (2021)
- [TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision](https://mlsanity.com/paper/2211.13090/) (2022)
- [Do Language Models Understand Time?](https://mlsanity.com/paper/2412.13845/) (2024)
- [Video Similarity and Alignment Learning on Partial Video Copy Detection](https://mlsanity.com/paper/2108.01817/) (2021)
- [Self-Supervised Video Similarity Learning](https://mlsanity.com/paper/2304.03378/) (2023)
- [A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection](https://mlsanity.com/paper/2203.02654/) (2022)
- [The 2023 Video Similarity Dataset and Challenge](https://mlsanity.com/paper/2306.09489/) (2023)
- [Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrieval](https://mlsanity.com/paper/2309.11091/) (2023)
- [A Survey of Video Datasets for Grounded Event Understanding](https://mlsanity.com/paper/2406.09646/) (2024)
- [VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression](https://mlsanity.com/paper/2303.08906/) (2023)
- [Self-supervised Video Retrieval Transformer Network](https://mlsanity.com/paper/2104.07993/) (2021)
- [3D-CSL: self-supervised 3D context similarity learning for Near-Duplicate Video Retrieval](https://mlsanity.com/paper/2211.05352/) (2022)
- [VRAG: Region Attention Graphs for Content-Based Video Retrieval](https://mlsanity.com/paper/2205.09068/) (2022)
- [VIDI: A Video Dataset of Incidents](https://mlsanity.com/paper/2205.13277/) (2022)
- [Video Timeline Modeling For News Story Understanding](https://mlsanity.com/paper/2309.13446/) (2023)
- [MUVR: A Multi-Modal Untrimmed Video Retrieval Benchmark with Multi-Level Visual Correspondence](https://mlsanity.com/paper/2510.21406/) (2025)
- [Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval](https://mlsanity.com/paper/2309.08167/) (2023)
- [The Effects of Short Video-Sharing Services on Video Copy Detection](https://mlsanity.com/paper/2403.18158/) (2024)
- [A Hierarchical Network-Oriented Analysis of User Participation in Misinformation Spread on WhatsApp](https://mlsanity.com/paper/2109.10462/) (2021)
- [A Toolchain for Comprehensive Audio/Video Analysis Using Deep Learning Based Multimodal Approach (A use case of riot or violent context detection)](https://mlsanity.com/paper/2407.03110/) (2024)
- [SOVABench: A Vehicle Surveillance Action Retrieval Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2601.04824/) (2026)
- [A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding](https://mlsanity.com/paper/2603.14733/) (2026)
- [Focus-to-Perceive Representation Learning: A Cognition-Inspired Hierarchical Framework for Endoscopic Video Analysis](https://mlsanity.com/paper/2603.25778/) (2026)
- [Efficient Logic Gate Networks for Video Copy Detection](https://mlsanity.com/paper/2604.21694/) (2026)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)

## Abstract (arXiv)

This paper introduces the problem of Fine-grained Incident Video Retrieval (FIVR). Given a query video, the objective is to retrieve all associated videos, considering several types of associations that range from duplicate videos to videos from the same incident. FIVR offers a single framework that contains several retrieval tasks as special cases. To address the benchmarking needs of all such tasks, we construct and present a large-scale annotated video dataset, which we call FIVR-200K, and it comprises 225,960 videos. To create the dataset, we devise a process for the collection of YouTube videos based on major news events from recent years crawled from Wikipedia and deploy a retrieval pipeline for the automatic selection of query videos based on their estimated suitability as benchmarks. We also devise a protocol for the annotation of the dataset with respect to the four types of video associations defined by FIVR. Finally, we report the results of an experimental study on the dataset comparing five state-of-the-art methods developed based on a variety of visual descriptors, highlighting the challenges of the current problem.