# VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression

arXiv: https://arxiv.org/abs/2303.08906 · HTML: https://mlsanity.com/paper/2303.08906/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 62 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Particular object retrieval with integral max-pooling of CNN activations](https://mlsanity.com/paper/1511.05879/) (2015)
- [Tempered Sigmoid Activations for Deep Learning with Differential Privacy](https://mlsanity.com/paper/2007.14191/) (2020)
- [Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward](https://mlsanity.com/paper/1801.00054/) (2017)
- [Video Summarization Using Fully Convolutional Sequence Networks](https://mlsanity.com/paper/1805.10538/) (2018)
- [Deep Multi-task Representation Learning: A Tensor Factorisation Approach](https://mlsanity.com/paper/1605.06391/) (2016)
- [ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning](https://mlsanity.com/paper/1908.07410/) (2019)
- [Discriminative Feature Learning for Unsupervised Video Summarization](https://mlsanity.com/paper/1811.09791/) (2018)
- [Video Summarization by Learning from Unpaired Data](https://mlsanity.com/paper/1805.12174/) (2018)
- [Reconstructive Sequence-Graph Network for Video Summarization](https://mlsanity.com/paper/2105.04066/) (2021)
- [Exploring global diverse attention via pairwise temporal relation for video summarization](https://mlsanity.com/paper/2009.10942/) (2020)
- [Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning](https://mlsanity.com/paper/2003.02546/) (2020)
- [FIVR: Fine-grained Incident Video Retrieval](https://mlsanity.com/paper/1809.04094/) (2018)
- [Combination of Multiple Global Descriptors for Image Retrieval](https://mlsanity.com/paper/1903.10663/) (2019)
- [Compact Descriptors for Video Analysis: the Emerging MPEG Standard](https://mlsanity.com/paper/1704.08141/) (2017)
- [UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection](https://mlsanity.com/paper/2111.14799/) (2021)
- [DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval](https://mlsanity.com/paper/2106.13266/) (2021)
- [Symmetrical Synthesis for Deep Metric Learning](https://mlsanity.com/paper/2001.11658/) (2020)
- [Circulant temporal encoding for video retrieval and temporal alignment](https://mlsanity.com/paper/1506.02588/) (2015)
- [Unsupervised Video Summarization via Multi-source Features](https://mlsanity.com/paper/2105.12532/) (2021)
- [ERA: Entity Relationship Aware Video Summarization with Wasserstein GAN](https://mlsanity.com/paper/2109.02625/) (2021)
- [VRAG: Region Attention Graphs for Content-Based Video Retrieval](https://mlsanity.com/paper/2205.09068/) (2022)
- [A Benchmark on Tricks for Large-scale Image Retrieval](https://mlsanity.com/paper/1907.11854/) (2019)

## Built on by (most cited first, 5 of 5)

- [Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis](https://mlsanity.com/paper/2312.11024/) (2023)
- [ConViS-Bench: Estimating Video Similarity Through Semantic Concepts](https://mlsanity.com/paper/2509.19245/) (2025)
- [Differentiable Resolution Compression and Alignment for Efficient Video Classification and Retrieval](https://mlsanity.com/paper/2309.08167/) (2023)
- [Atom: Efficient On-Device Video-Language Pipelines Through Modular Reuse](https://mlsanity.com/paper/2512.17108/) (2025)
- [Generative Uncertainty as a Self-supervised Signal for Semantic Similarity Learning](https://mlsanity.com/paper/2609.35341/) (2026)

## Abstract (arXiv)

In content-based video retrieval (CBVR), dealing with large-scale collections, efficiency is as important as accuracy; thus, several video-level feature-based studies have actively been conducted. Nevertheless, owing to the severe difficulty of embedding a lengthy and untrimmed video into a single feature, these studies have been insufficient for accurate retrieval compared to frame-level feature-based studies. In this paper, we show that appropriate suppression of irrelevant frames can provide insight into the current obstacles of the video-level approaches. Furthermore, we propose a Video-to-Video Suppression network (VVS) as a solution. VVS is an end-to-end framework that consists of an easy distractor elimination stage to identify which frames to remove and a suppression weight generation stage to determine the extent to suppress the remaining frames. This structure is intended to effectively describe an untrimmed video with varying content and meaningless information. Its efficacy is proved via extensive experiments, and we show that our approach is not only state-of-the-art in video-level approaches but also has a fast inference time despite possessing retrieval capabilities close to those of frame-level approaches. Code is available at https://github.com/sejong-rcv/VVS