# SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection

arXiv: https://arxiv.org/abs/2206.12634 · HTML: https://mlsanity.com/paper/2206.12634/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 8 references that are in the map.
- First posted on arXiv 25 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Video Classification with Channel-Separated Convolutional Networks](https://mlsanity.com/paper/1904.02811/) (2019)
- [Large-scale weakly-supervised pre-training for video action recognition](https://mlsanity.com/paper/1905.00561/) (2019)
- [Generic Event Boundary Detection: A Benchmark for Event Segmentation](https://mlsanity.com/paper/2101.10511/) (2021)
- [Structured Context Transformer for Generic Event Boundary Detection](https://mlsanity.com/paper/2206.02985/) (2022)

## Built on by (most cited first, 4 of 4)

- [Rethinking the Architecture Design for Efficient Generic Event Boundary Detection](https://mlsanity.com/paper/2407.12622/) (2024)
- [What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection](https://mlsanity.com/paper/2404.18935/) (2024)
- [Motion Aware Self-Supervision for Generic Event Boundary Detection](https://mlsanity.com/paper/2210.05574/) (2022)
- [AutoMatch: A Large-scale Audio Beat Matching Benchmark for Boosting Deep Learning Assistant Video Editing](https://mlsanity.com/paper/2303.01884/) (2023)

## Abstract (arXiv)

This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Context Transformer (SC-Transformer) method for GEBD. Specifically, a transformer decoder module is added after transformer encoders to extract high quality frame features. The final classification is performed jointly on the results of the original binary classifier and a newly introduced multi-class classifier branch. To enrich motion information, optical flow is introduced as a new modality. Finally, model ensemble is used to further boost performance. The proposed method achieves 86.49% F1 score on Kinetics-GEBD test set. which improves 2.86% F1 score compared to the previous SOTA method.