# Masked Autoencoders for Generic Event Boundary Detection CVPR'2022 Kinetics-GEBD Challenge

arXiv: https://arxiv.org/abs/2206.08610 · HTML: https://mlsanity.com/paper/2206.08610/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 13 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 12 of 12)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Masked Autoencoders As Spatiotemporal Learners](https://mlsanity.com/paper/2205.09113/) (2022)
- [Multimodal Masked Autoencoders Learn Transferable Representations](https://mlsanity.com/paper/2205.14204/) (2022)
- [Generic Event Boundary Detection: A Benchmark for Event Segmentation](https://mlsanity.com/paper/2101.10511/) (2021)
- [Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction](https://mlsanity.com/paper/2206.00790/) (2022)
- [Winning the CVPR'2021 Kinetics-GEBD Challenge: Contrastive Learning Approach](https://mlsanity.com/paper/2106.11549/) (2021)
- [Generic Event Boundary Detection Challenge at CVPR 2021 Technical Report: Cascaded Temporal Attention Network (CASTANET)](https://mlsanity.com/paper/2107.00239/) (2021)
- [Improved Hard Example Mining Approach for Single Shot Object Detectors](https://mlsanity.com/paper/2202.13080/) (2022)

## Built on by (most cited first, 3 of 3)

- [Online Generic Event Boundary Detection](https://mlsanity.com/paper/2510.06855/) (2025)
- [MAE-GEBD:Winning the CVPR'2023 LOVEU-GEBD Challenge](https://mlsanity.com/paper/2306.15704/) (2023)
- [Learning Brain Tumor Representation in 3D High-Resolution MR Images via Interpretable State Space Models](https://mlsanity.com/paper/2409.07746/) (2024)

## Abstract (arXiv)

Generic Event Boundary Detection (GEBD) tasks aim at detecting generic, taxonomy-free event boundaries that segment a whole video into chunks. In this paper, we apply Masked Autoencoders to improve algorithm performance on the GEBD tasks. Our approach mainly adopted the ensemble of Masked Autoencoders fine-tuned on the GEBD task as a self-supervised learner with other base models. Moreover, we also use a semi-supervised pseudo-label method to take full advantage of the abundant unlabeled Kinetics-400 data while training. In addition, we propose a soft-label method to partially balance the positive and negative samples and alleviate the problem of ambiguous labeling in this task. Lastly, a tricky segmentation alignment policy is implemented to refine boundaries predicted by our models to more accurate locations. With our approach, we achieved 85.94% on the F1-score on the Kinetics-GEBD test set, which improved the F1-score by 2.31% compared to the winner of the 2021 Kinetics-GEBD Challenge. Our code is available at https://github.com/ContentAndMaterialPortrait/MAE-GEBD.