Paper#Video Understanding, Vision-Language Models, Scene Segmentation🔬 ResearchAnalyzed: Jan 4, 2026 00:06
Scene-VLM: Video Scene Segmentation with Vision-Language Models
Published:Dec 25, 2025 20:31
•1 min read
•ArXiv
Analysis
This paper introduces Scene-VLM, a novel approach to video scene segmentation using fine-tuned vision-language models. It addresses limitations of existing methods by incorporating multimodal cues (frames, transcriptions, metadata), enabling sequential reasoning, and providing explainability. The model's ability to generate natural-language rationales and achieve state-of-the-art performance on benchmarks highlights its significance.
Key Takeaways
- •Scene-VLM is the first fine-tuned vision-language model for video scene segmentation.
- •It leverages multimodal cues (frames, transcriptions, metadata) for improved scene understanding.
- •The model enables sequential reasoning and provides explainability through natural language rationales.
- •Scene-VLM achieves state-of-the-art performance on standard scene segmentation benchmarks.
Reference
“Scene-VLM yields significant improvements of +6 AP and +13.7 F1 over the previous leading method on MovieNet.”