# TransNet V2: An effective deep network architecture for fast shot transition detection

arXiv: https://arxiv.org/abs/2008.04838 · HTML: https://mlsanity.com/paper/2008.04838/

## Key facts

- Cited by 171 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 30 of 171)

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- [CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models](https://mlsanity.com/paper/2508.11484/) (2025)
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- [AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection](https://mlsanity.com/paper/2304.06116/) (2023)
- [ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models](https://mlsanity.com/paper/2506.21356/) (2025)
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## Abstract (arXiv)

Although automatic shot transition detection approaches are already investigated for more than two decades, an effective universal human-level model was not proposed yet. Even for common shot transitions like hard cuts or simple gradual changes, the potential diversity of analyzed video contents may still lead to both false hits and false dismissals. Recently, deep learning-based approaches significantly improved the accuracy of shot transition detection using 3D convolutional architectures and artificially created training data. Nevertheless, one hundred percent accuracy is still an unreachable ideal. In this paper, we share the current version of our deep network TransNet V2 that reaches state-of-the-art performance on respected benchmarks. A trained instance of the model is provided so it can be instantly utilized by the community for a highly efficient analysis of large video archives. Furthermore, the network architecture, as well as our experience with the training process, are detailed, including simple code snippets for convenient usage of the proposed model and visualization of results.