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arXiv Computer Vision By Tingyu Lin, Christian Stippel, Armin Dadras, Jakob Zenzmaier, Florian Kleber, Wolfgang Aigner, Robert Sablatnig

PERSIST: Persistent-State Discrimination for Shot Boundary Detection

Read the original on arXiv Computer Vision →

PERSIST redefines shot boundary detection as a task of semantic discrimination, requiring a persistent update of a video’s latent temporal state rather than a transient visual change. It employs a FiLM‑conditioned sinusoidal representation network and a structured discriminator that fuses local change, transient impulse, and return‑to‑trend cues into a single interpretable per‑frame signal. The method achieves comparable recall to leading detectors while significantly reducing false positives from flash, text overlay, and archival artifacts, and it is trained solely on real transitions from ClipShots.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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