# PERSIST: Persistent-State Discrimination for Shot Boundary Detection

arXiv: https://arxiv.org/abs/2608.29287 · HTML: https://mlsanity.com/paper/2608.29287/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 34 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019)
- [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018)
- [MovieNet: A Holistic Dataset for Movie Understanding](https://mlsanity.com/paper/2007.10937/) (2020)
- [TransNet V2: An effective deep network architecture for fast shot transition detection](https://mlsanity.com/paper/2008.04838/) (2020)
- [A Local-to-Global Approach to Multi-modal Movie Scene Segmentation](https://mlsanity.com/paper/2004.02678/) (2020)
- [Shot Contrastive Self-Supervised Learning for Scene Boundary Detection](https://mlsanity.com/paper/2104.13537/) (2021)
- [AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection](https://mlsanity.com/paper/2304.06116/) (2023)
- [Boundary-aware Self-supervised Learning for Video Scene Segmentation](https://mlsanity.com/paper/2201.05277/) (2022)
- [Fast Video Shot Transition Localization with Deep Structured Models](https://mlsanity.com/paper/1808.04234/) (2018)
- [TransNet: A deep network for fast detection of common shot transitions](https://mlsanity.com/paper/1906.03363/) (2019)
- [Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks](https://mlsanity.com/paper/1705.03281/) (2017)
- [Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks](https://mlsanity.com/paper/1705.08214/) (2017)
- [OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer](https://mlsanity.com/paper/2604.24762/) (2026)
- [TransVLM: A Vision-Language Framework and Benchmark for Detecting Any Shot Transitions](https://mlsanity.com/paper/2604.27975/) (2026)

## Abstract (arXiv)

Shot boundary detection (SBD) is widely treated as the localisation of local visual discontinuities, yet many false positives such as hand-held shake, illumination flicker, motion blur, occlusion, and damaged archival material produce equally sharp local change without introducing a new shot. We reformulate SBD as boundary semantic discrimination: a frame is favoured as a boundary only when its local change evidence is accompanied by a persistent update of the video's latent temporal state, rather than a transient excursion that returns to the surrounding trend. This persistence test is operationalised with a continuous latent state from a FiLM-conditioned sinusoidal representation network and a structured discriminator that combines three semantic cues, local change, transient impulse, and return-to-trend, into a single interpretable per-frame signal over a dual-rate temporal backbone. The resulting framework, PERSIST, turns every decision into an inspectable one: the persistence criterion is trained into the classifier, its per-frame effect stays readable from the gate triple, and its learned latent state is measurably boundary-discriminative. On a 2,727-video per-subtype diagnostic it removes 33-80% of flash, text-overlay, and archival false positives relative to an identically trained cue detector, and at matched true-transition recall it roughly halves TransNetV2's pseudo-event false positives on that diagnostic and cuts its false positives on ClipShots footage by about a quarter, while preserving recall. It does so while reaching parity with the strongest public detector across online, broadcast, short-form, and historical-archive transfer evaluations, under markedly stricter training: it learns from ClipShots real transitions only, whereas the anchor draws on additional corpora whose transitions are 85% synthetic. Code is available at https://github.com/linty5/PERSIST.