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

TL;DR

TransNet V2 is a deep learning model that achieves state-of-the-art accuracy in fast shot transition detection, offering an efficient tool for analyzing large video archives with detailed architecture and training insights.

Contribution

The paper introduces TransNet V2, a novel deep network architecture that improves shot transition detection accuracy and provides practical implementation details and trained models for community use.

Findings

Achieves state-of-the-art performance on benchmark datasets

Provides a trained model for instant application

Details architecture and training process for reproducibility

Abstract

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.