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

Source: [SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection](<https://www.sota2.com/research/paper/68fa6ce51e209131cea36994>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:16:31.054Z

Authors: Dexiang Hong, Xiaoqi Ma, Xinyao Wang, Congcong Li, Yufei Wang, Longyin Wen

Published: 2022-06-25T12:27:13.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2206.12634>)

[PDF](<https://arxiv.org/pdf/2206.12634.pdf>)