[Paper] OmniShotCut: Holistic Relational Shot Boundary Detection with Shot-Query Transformer

Source: arXiv - 2604.24762v1

Overview

The paper OmniShotCut re‑thinks Shot Boundary Detection (SBD) – the task of automatically splitting a video into its constituent shots – as a structured relational problem. By introducing a “shot‑query” Transformer that reasons about both intra‑shot continuity and inter‑shot transitions, the authors achieve more accurate and interpretable boundaries. They also release a synthetic data pipeline and a new benchmark (OmniShotCutBench) that address the long‑standing issues of noisy labels and outdated test sets.

Key Contributions

• Shot‑Query Transformer: A dense video Transformer that treats each potential shot as a query, jointly predicting shot extents and the relational cues that link neighboring shots.
• Holistic Relational Formulation: Simultaneous modeling of intra‑shot consistency and inter‑shot discontinuities, enabling the detection of subtle transitions (e.g., fades, dissolves) that traditional classifiers often miss.
• Synthetic Transition Generator: A fully automated pipeline that creates realistic transition clips (cuts, fades, wipes, etc.) with exact ground‑truth boundaries, eliminating reliance on noisy human annotations.
• OmniShotCutBench: A modern, wide‑domain benchmark covering diverse genres, resolutions, and frame rates, designed for both overall performance and diagnostic analysis of specific transition types.
• Interpretability Tools: Visualization of the learned relational graphs, giving developers insight into why a particular boundary was chosen.

Methodology

1. Shot Queries: The video is first tokenized into short clip embeddings (e.g., 0.5‑second windows). Each embedding serves as a query that asks the Transformer: “What is the start and end of the shot I belong to?”
2. Dense Transformer Encoder: A multi‑head self‑attention stack processes the entire sequence, allowing each query to attend to all other clips. This global view captures long‑range dependencies needed for gradual transitions.
3. Relational Heads: Two parallel prediction heads are attached:
   • Intra‑shot head predicts a binary mask indicating whether neighboring clips belong to the same shot.
   • Inter‑shot head predicts a transition type (cut, fade, wipe, etc.) and a confidence score.
4. Joint Loss: A combination of segmentation loss (for shot masks) and classification loss (for transition types) is optimized end‑to‑end. Because the synthetic data provides exact timestamps, the loss can be computed with pixel‑level precision.
5. Synthetic Data Generation: Using a library of raw video clips, the authors programmatically apply transition effects with controllable parameters (duration, opacity curves, motion paths). This yields millions of labeled examples covering the full taxonomy of transitions.

Results & Findings

• Benchmark Performance: On OmniShotCutBench, the proposed model outperforms prior SBD state‑of‑the‑art methods by +12.4% F1 on gradual transitions and +8.7% F1 on hard‑to‑detect cuts.
• Boundary Precision: The average temporal offset between predicted and ground‑truth boundaries drops from ~6 frames (baseline) to ≈1.2 frames, a 5× improvement.
• Robustness Across Domains: Experiments on unseen domains (e.g., sports, animation, user‑generated content) show less than 3% performance degradation, confirming the model’s generalization.
• Interpretability: Visualizations of the relational graph reveal that the model explicitly learns “soft” connections across frames during fades, which aligns with human intuition.

Practical Implications

• Video Editing Pipelines: Automated shot detection with near‑frame accuracy can power smarter timeline segmentation in editing tools (e.g., Adobe Premiere, DaVinci Resolve), reducing manual trimming effort.
• Content Moderation & Indexing: Accurate shot boundaries enable more reliable scene‑level tagging, thumbnail generation, and ad‑insertion logic for streaming platforms.
• Machine‑Generated Media: For AI‑generated videos (deepfakes, synthetic news), reliable SBD can serve as a quality‑control checkpoint, flagging unnatural transitions.
• Edge Deployment: The Transformer architecture can be distilled or quantized for on‑device inference, making real‑time shot detection feasible on mobile cameras or embedded surveillance units.

Limitations & Future Work

• Synthetic‑Real Gap: Although the synthetic pipeline covers many transition families, subtle artifacts present in real‑world footage (e.g., sensor noise, compression glitches) may still challenge the model.
• Computational Cost: The dense Transformer scales quadratically with video length, which can be prohibitive for hour‑long footage without further optimization (e.g., hierarchical attention).
• Transition Taxonomy: The current set of transition types is fixed; extending to exotic effects (e.g., custom wipes, AI‑generated morphs) will require additional synthesis rules.
• Future Directions: The authors suggest exploring sparse attention mechanisms, domain‑adaptive fine‑tuning on a small set of real transitions, and integrating audio cues to improve boundary detection in noisy visual conditions.

Authors

• Boyang Wang
• Guangyi Xu
• Zhipeng Tang
• Jiahui Zhang
• Zezhou Cheng

Paper Information

• arXiv ID: 2604.24762v1
• Categories: cs.CV
• Published: April 27, 2026
• PDF: Download PDF