OpenNovelty.org

Video Scene Segmentation with Genre and Duration Signals

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Video Scene SegmentationMovie Scene Boundary DetectionVideo Temporal Segmentation
Abstract:

Video scene segmentation aims to detect semantically coherent boundaries in long-form videos, bridging the gap between low-level visual signals and high-level narrative understanding. However, existing methods primarily rely on visual similarity between adjacent shots, which makes it difficult to accurately identify scene boundaries, especially when semantic transitions do not align with visual changes. In this paper, we propose a novel approach that incorporates production-level metadata, specifically genre conventions and shot duration patterns, into video scene segmentation. Our main contributions are three-fold: (1) we leverage textual genre definitions as semantic priors to guide shot-level representation learning during self-supervised pretraining, enabling better capture of narrative coherence; (2) we introduce a duration-aware anchor selection strategy that prioritizes shorter shots based on empirical duration statistics, improving pseudo-boundary generation quality; (3) we propose a test-time shot splitting strategy that subdivides long shots into segments for improved temporal modeling. Experimental results demonstrate state-of-the-art performance on MovieNet-SSeg and BBC datasets. We introduce MovieChat-SSeg, extending MovieChat-1K with manually annotated scene boundaries across 1,000 videos spanning movies, TV series, and documentaries.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a metadata-guided approach to video scene segmentation, incorporating genre conventions and shot duration patterns into shot representation learning and boundary detection. It resides in the 'Metadata-Guided and Rule-Based Methods' leaf of the taxonomy, which contains only three papers total. This is a relatively sparse research direction compared to neighboring leaves like 'Self-Supervised and Contrastive Learning Approaches' (three papers) or 'Transformer-Based Global Context Modeling' (two papers), suggesting that metadata-driven strategies remain underexplored within the broader scene boundary detection landscape.

The taxonomy reveals that most scene boundary detection work clusters around data-driven methods: self-supervised contrastive learning, transformer-based global context modeling, and multimodal fusion approaches. The paper's sibling works in the same leaf include rule-based boundary detection and other metadata-guided strategies, but the broader field emphasizes learned representations from visual or temporal features. The paper's use of textual genre definitions and duration statistics positions it at the intersection of metadata-guided methods and self-supervised learning, bridging two distinct branches of the taxonomy tree.

Among twelve candidates examined across three contributions, no clearly refuting prior work was identified. The genre-guided shot representation contribution examined two candidates with no refutations; the duration-aware anchor selection examined zero candidates; and the test-time shot splitting examined ten candidates with no refutations. This limited search scope suggests that within the top-ranked semantic matches, no direct overlap was found, though the small candidate pool (twelve total) means the analysis does not cover the full breadth of related work in metadata-driven segmentation or shot-level modeling.

Given the sparse taxonomy leaf and limited literature search, the work appears to occupy a relatively unexplored niche within scene segmentation. However, the analysis is constrained by the small candidate pool and does not exhaustively cover all metadata-guided or duration-based methods. The absence of refuting candidates among twelve examined papers suggests novelty within the top-ranked semantic neighborhood, but broader manual review would be needed to assess overlap with rule-based or production-metadata approaches outside this search scope.

Taxonomy

Core-task Taxonomy Papers
44
3
Claimed Contributions
12
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Video scene segmentation in long-form narrative videos. The field encompasses diverse approaches to partitioning extended video content into semantically coherent units. The taxonomy reveals several major branches: Scene Boundary Detection Methods focus on identifying transitions through visual, temporal, or metadata cues; Long-Form Video Understanding and Retrieval addresses challenges in processing and querying extended sequences; Narrative Structure Extraction and Analysis targets higher-level story organization; while Video Generation and Composition, Audio-Visual Content Generation and Analysis, and Application-Specific Segmentation and Indexing explore synthesis, multimodal integration, and domain-tailored solutions. Representative works like SALOVA[1] and SceneRAG[4] illustrate how retrieval-oriented methods leverage scene-level representations, while Kernel Temporal Segmentation[5] and Semantic Transition Detection[3] exemplify boundary detection strategies that rely on learned temporal patterns. Within Scene Boundary Detection Methods, a key contrast emerges between data-driven approaches that learn transitions from visual or semantic features and metadata-guided or rule-based strategies that exploit external information such as genre conventions or script alignment. Genre Duration Segmentation[0] sits within the Metadata-Guided and Rule-Based Methods cluster, alongside Rule Based Boundaries[13], emphasizing the use of domain-specific heuristics or structured metadata to inform segmentation decisions. This contrasts with purely learned methods like Scene Detection Policies[2] or Semantic Transition Detection[3], which infer boundaries from content alone. The metadata-guided approach offers interpretability and can incorporate prior knowledge about narrative structure, though it may require additional annotation or domain expertise. Open questions remain about how to best combine rule-based priors with adaptive learning, and how metadata-driven methods scale across diverse genres and production styles.

Claimed Contributions

Genre-guided shot representation learning using textual definitions

The authors propose encoding genre conventions through textual descriptions from IMDb definitions and integrating them into shot representation learning via affinity-based residual connections in a ViT architecture. This approach provides semantic guidance during pretraining to capture narrative coherence beyond visual features alone.

2 retrieved papers
Duration-aware anchor selection strategy for pseudo-boundary generation

The authors introduce a sampling strategy that assigns higher probabilities to shorter shots when selecting anchors for pseudo-boundary generation during self-supervised pretraining. This approach generates more diverse training samples compared to fixed-anchor methods by leveraging shot duration patterns.

0 retrieved papers
Test-time shot splitting strategy for long shots

The authors propose a preprocessing strategy that subdivides shots exceeding a duration threshold (10 seconds) into smaller segments during inference. This approach requires no model retraining and can be applied to existing scene segmentation frameworks.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Genre-guided shot representation learning using textual definitions

The authors propose encoding genre conventions through textual descriptions from IMDb definitions and integrating them into shot representation learning via affinity-based residual connections in a ViT architecture. This approach provides semantic guidance during pretraining to capture narrative coherence beyond visual features alone.

Contribution

Duration-aware anchor selection strategy for pseudo-boundary generation

The authors introduce a sampling strategy that assigns higher probabilities to shorter shots when selecting anchors for pseudo-boundary generation during self-supervised pretraining. This approach generates more diverse training samples compared to fixed-anchor methods by leveraging shot duration patterns.

Contribution

Test-time shot splitting strategy for long shots

The authors propose a preprocessing strategy that subdivides shots exceeding a duration threshold (10 seconds) into smaller segments during inference. This approach requires no model retraining and can be applied to existing scene segmentation frameworks.

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