Problem
Research questions and friction points this paper is trying to address.
Shot Boundary Detection
False Positives
Persistent-State Discrimination
Local Visual Discontinuities
Semantic Cues
PERSIST: Persistent-State Discrimination for Shot Boundary Detection
🤖 AI Summary
为解决因手持抖动、光照闪烁等因素导致的误判，本文提出PERSIST框架，通过持续状态判别法结合局部变化等语义线索进行镜头边界检测。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Persistent-State Discrimination
Shot Boundary Detection
FiLM-Conditioned Sinusoidal Representation
Structured Discriminator
Dual-Rate Temporal Backbone
🔎 Similar Papers
No similar papers found.
