AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection (2304.06116v1)

Abstract: The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou (Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way we consume and create content. For video content creation and understanding, the shot boundary detection (SBD) is one of the most essential components in various scenarios. In this work, we release a new public Short video sHot bOundary deTection dataset, named SHOT, consisting of 853 complete short videos and 11,606 shot annotations, with 2,716 high quality shot boundary annotations in 200 test videos. Leveraging this new data wealth, we propose to optimize the model design for video SBD, by conducting neural architecture search in a search space encapsulating various advanced 3D ConvNets and Transformers. Our proposed approach, named AutoShot, achieves higher F1 scores than previous state-of-the-art approaches, e.g., outperforming TransNetV2 by 4.2%, when being derived and evaluated on our newly constructed SHOT dataset. Moreover, to validate the generalizability of the AutoShot architecture, we directly evaluate it on another three public datasets: ClipShots, BBC and RAI, and the F1 scores of AutoShot outperform previous state-of-the-art approaches by 1.1%, 0.9% and 1.2%, respectively. The SHOT dataset and code can be found in https://github.com/wentaozhu/AutoShot.git .

• The paper introduces the SHOT dataset with 853 videos and 11,606 shot annotations specifically designed for short video analysis.
• The paper leverages Neural Architecture Search with 3D ConvNets and Transformers to develop the AutoShot model for enhanced shot boundary detection.
• The paper demonstrates that AutoShot outperforms state-of-the-art methods by up to 4.2% in F1 score, highlighting its strong generalizability.

Overview of "AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection"

The paper "AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection" introduces both a novel dataset and a neural architecture designed for shot boundary detection (SBD) in short videos. This work is timely and relevant due to the burgeoning popularity of short-form videos on platforms such as TikTok, Instagram Reels, and YouTube Shorts. The AutoShot framework leverages a newly constructed dataset, SHOT, specifically designed for fine-tuning and evaluating SBD systems.

Contributions

1. SHOT Dataset: The authors have compiled a specialized dataset for short video SBD, termed SHOT, which consists of 853 videos with 11,606 shot annotations. This dataset is one of the first extensive collections focusing on short videos and includes a test set annotated thoroughly by experts to ensure high quality. The dataset reveals unique challenges in short video analysis due to the rapid pace and diverse contexts of content transitions.
2. Neural Architecture Search (NAS): Leveraging the SHOT dataset, the authors propose an advanced NAS framework encapsulating a search space of 3D ConvNets and Transformers to automatically design an effective architecture for SBD. This architecture, named AutoShot, is specifically fine-tuned through single path one-shot SuperNet training and Bayesian optimization.
3. Performance Evaluation: AutoShot achieves a substantial performance boost over existing state-of-the-art methods. It improves F1 scores over TransNetV2 by 4.2% on SHOT, and also shows superior generalizability by outperforming previous best approaches on other public datasets such as ClipShots, BBC, and RAI by 1.1%, 0.9%, and 1.2%, respectively.

Methodology

The researchers designed a flexible search space comprising different convolution layers and Transformer blocks. Four types of search block configurations – DDCNNV2, DDCNNV2A, DDCNNV2B, and DDCNNV2C – were explored, offering a wide diversity of spatio-temporal feature extraction methodologies. The NAS process is then utilized to search this extensive architecture space for the optimal configuration that maximizes performance on short video SBD tasks.

Results and Implications

The success of AutoShot indicates two significant points for the field of video analysis:

1. The tailored neural architectures discovered through NAS can significantly enhance the accuracy of detecting shot boundaries, especially in short, rapidly evolving video content.
2. The dataset provides a benchmark that allows for better assessments and development of future short video content analytics tools.

In practical terms, advancements in SBD can lead to improved video structuring, automated editing, and enhanced content recommendation systems in commercial short video platforms. The paper suggests a shift towards specialized neural architectures that can be automatically adapted and optimized for specific modalities such as short-form video.

Future Directions

As the field progresses, further exploration into NAS frameworks that incorporate pure Transformer architectures might yield even more robust results. Additionally, expanding the SHOT dataset with more diverse content and including multi-modal data could offer deeper insights. The methodology and findings of this paper lay the groundwork for numerous future applications and explorations in the domain of video analysis.