PERSIST: Persistent-State Discrimination for Shot Boundary Detection

• URL: http://arxiv.org/abs/2608.29287v1
• Date: Sat, 29 Aug 2026 14:23:00 GMT
• Title: PERSIST: Persistent-State Discrimination for Shot Boundary Detection
• Abstract summary: Shot boundary detection (SBD) is widely treated as the localisation of local visual discontinuities.<n>We reformulate SBD as boundary semantic discrimination.<n>PerSIST turns every decision into an inspectable one.
• Score: 2.2822910284300355
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• On the Adversarial Robustness of Remote Sensing Semantic Change Detection [19.4097466391406]
  Semantic change detection (SCD) is a bitemporal dense-prediction task that jointly identifies changed regions and their semantic states before and after change.<n>We present a task-specific evaluation framework that separates output-side attack objectives from input-side temporal perturbation access.<n>Experiments show that final semantic-change predictions can be severely corrupted even when binary change localization remains comparatively stable.
  arXiv  Detail & Related papers  (2026-08-15T15:02:42Z)
• Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks [68.64050157343334]
  Existing watermarks preserve provenance under editing, but this same robustness allows an adversary to alter critical content while retaining attribution.<n>We introduce an innovative watermark that jointly provides provenance and tamper evidence.<n>The signals share the same mechanism but use independent keys and different seeding windows over normalized text, making one resilient to edits and the other sensitive to reader-visible changes.
  arXiv  Detail & Related papers  (2026-08-13T01:55:16Z)
• Conformal Coverage Guarantees for Any Video Temporal Grounder [2.3982445219832678]
  Event boundaries in continuous video are ambiguous: re-annotate the same query-video pair and independent annotators mark moments that overlap by less than half on a large fraction of samples.<n> COVER changes the output object: a model-agnostic wrapper that turns any grounder, a trained localizer or a black-box video-language model.<n>Two score families, a two-sided boundary-widening score for grounders that emit an interval and a super-level-set score for grounders that emit a relevance signal.
  arXiv  Detail & Related papers  (2026-08-07T17:19:27Z)
• SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch-Level Incoherence and Temporal Roughness [4.774658730084455]
  We introduce a training-free detector that operates on patch tokens from a frozen vision encoder to detect both fully generated and partially edited videos.<n>SPLIT computes two complementary signals: Two-step Temporal Roughness (TTR) and Local Spatial Motion Incoherence (LSMI)<n>We propose a service-aligned evaluation protocol based on Fake Recall at fixed FPR with real-only threshold calibration and cross-real threshold transfer.
  arXiv  Detail & Related papers  (2026-07-03T02:34:50Z)
• Recovering Input Text from Hidden States: Study of Gradient-Based Inversion of Decoder-Only Language Models [0.0]
  We study the hidden-state inversion problem as a continuous embedding-space optimisation.<n>We analyse which tokens break the reconstructions and find a sharp categorical asymmetry.<n>Last-layer hidden states of GPT-2 are as sensitive as original text.
  arXiv  Detail & Related papers  (2026-07-01T12:18:12Z)
• Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation [2.65131397683555]
  Real-time video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image-guided interventions.<n>We present a Temporally Consistent Learning Framework that distills temporal coherence into a 2D network during training.<n>Our design is driven by a key clinical observation: the prostate exhibits geometric stability, whereas the surrounding acoustic environment fluctuates due to physiological motion and transducer pressure.
  arXiv  Detail & Related papers  (2026-06-30T06:27:02Z)
• Temporal Backtracking Search for Test-time Generative Video Reasoning [51.4821857044108]
  generative video reasoning remains bottlenecked by a single-shot paradigm.<n>We introduce Temporal Backtracking Search (TBS), which shifts the search space to the temporal axis.<n>In a strict out-of-distribution setting, TBS achieves 22.7%, with every solved episode stemming from a restarted branch.
  arXiv  Detail & Related papers  (2026-06-11T19:41:18Z)
• PROBE: Diagnosing Residual Concept Capacity in Erased Text-to-Video Diffusion Models [9.957850061310054]
  Concept techniques for text-to-video (T2V) diffusion models report substantial suppression of sensitive content.<n>We introduce PROBE, a diagnostic protocol that quantifies the textitre potential erased concepts in T2V models.
  arXiv  Detail & Related papers  (2026-03-23T04:01:13Z)
• FILT3R: Latent State Adaptive Kalman Filter for Streaming 3D Reconstruction [51.56484100374058]
  Streaming 3D reconstruction maintains a persistent latent state that is updated online from incoming frames.<n>FILT3R casts recurrent state updates as state estimation in token space.<n>Code will be released at https://github.com/jinotter3/FILT3R.
  arXiv  Detail & Related papers  (2026-03-19T04:56:36Z)
• Morphing Through Time: Diffusion-Based Bridging of Temporal Gaps for Robust Alignment in Change Detection [51.56484100374058]
  We introduce a modular pipeline that improves spatial and temporal robustness without altering existing change detection networks.<n>A diffusion module synthesizes intermediate morphing frames that bridge large appearance gaps, enabling RoMa to estimate stepwise correspondences.<n>Experiments on LEVIR-CD, WHU-CD, and DSIFN-CD show consistent gains in both registration accuracy and downstream change detection.
  arXiv  Detail & Related papers  (2025-11-11T08:40:28Z)
• Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization [60.73623588349311]
  We propose a universal context-aware contrastive learning framework (UniCaCLF) for temporal forgery localization.<n>Our approach leverages supervised contrastive learning to discover and identify forged instants by means of anomaly detection.<n>An efficient context-aware contrastive coding is introduced to further push the limit of instant feature distinguishability between genuine and forged instants.
  arXiv  Detail & Related papers  (2025-06-10T06:40:43Z)
• RIGID: Recurrent GAN Inversion and Editing of Real Face Videos [73.97520691413006]
  GAN inversion is indispensable for applying the powerful editability of GAN to real images. Existing methods invert video frames individually often leading to undesired inconsistent results over time. We propose a unified recurrent framework, named textbfRecurrent vtextbfIdeo textbfGAN textbfInversion and etextbfDiting (RIGID) Our framework learns the inherent coherence between input frames in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-08-11T12:17:24Z)
• Boosting Continuous Sign Language Recognition via Cross Modality Augmentation [135.30357113518127]
  Continuous sign language recognition deals with unaligned video-text pair. We propose a novel architecture with cross modality augmentation. The proposed framework can be easily extended to other existing CTC based continuous SLR architectures.
  arXiv  Detail & Related papers  (2020-10-11T15:07:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.