# Winning the CVPR'2021 Kinetics-GEBD Challenge: Contrastive Learning Approach

arXiv: https://arxiv.org/abs/2106.11549 · HTML: https://mlsanity.com/paper/2106.11549/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 25 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 18 of 18)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [G-TAD: Sub-Graph Localization for Temporal Action Detection](https://mlsanity.com/paper/1911.11462/) (2019)
- [Graph Convolutional Networks for Temporal Action Localization](https://mlsanity.com/paper/1909.03252/) (2019)
- [CDC: Convolutional-De-Convolutional Networks for Precise Temporal Action Localization in Untrimmed Videos](https://mlsanity.com/paper/1703.01515/) (2017)
- [End-to-end Learning of Action Detection from Frame Glimpses in Videos](https://mlsanity.com/paper/1511.06984/) (2015)
- [Fast Learning of Temporal Action Proposal via Dense Boundary Generator](https://mlsanity.com/paper/1911.04127/) (2019)
- [Boundary Content Graph Neural Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/2008.01432/) (2020)
- [Temporal Recurrent Networks for Online Action Detection](https://mlsanity.com/paper/1811.07391/) (2018)
- [TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks](https://mlsanity.com/paper/2011.11479/) (2020)
- [Online Action Detection](https://mlsanity.com/paper/1604.06506/) (2016)
- [Counting Out Time: Class Agnostic Video Repetition Counting in the Wild](https://mlsanity.com/paper/2006.15418/) (2020)
- [Generic Event Boundary Detection: A Benchmark for Event Segmentation](https://mlsanity.com/paper/2101.10511/) (2021)
- [CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016](https://mlsanity.com/paper/1608.00797/) (2016)

## Built on by (most cited first, 13 of 13)

- [Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection](https://mlsanity.com/paper/2112.04771/) (2021)
- [End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection](https://mlsanity.com/paper/2203.15336/) (2022)
- [Structured Context Transformer for Generic Event Boundary Detection](https://mlsanity.com/paper/2206.02985/) (2022)
- [Rethinking the Architecture Design for Efficient Generic Event Boundary Detection](https://mlsanity.com/paper/2407.12622/) (2024)
- [Soft-Landing Strategy for Alleviating the Task Discrepancy Problem in Temporal Action Localization Tasks](https://mlsanity.com/paper/2211.06023/) (2022)
- [What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection](https://mlsanity.com/paper/2404.18935/) (2024)
- [Fine-grained Dynamic Network for Generic Event Boundary Detection](https://mlsanity.com/paper/2407.04274/) (2024)
- [Masked Autoencoders for Generic Event Boundary Detection CVPR'2022 Kinetics-GEBD Challenge](https://mlsanity.com/paper/2206.08610/) (2022)
- [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)
- [Local Compressed Video Stream Learning for Generic Event Boundary Detection](https://mlsanity.com/paper/2309.15431/) (2023)
- [MAE-GEBD:Winning the CVPR'2023 LOVEU-GEBD Challenge](https://mlsanity.com/paper/2306.15704/) (2023)
- [Structured Context Learning for Generic Event Boundary Detection](https://mlsanity.com/paper/2512.00475/) (2025)

## Abstract (arXiv)

Generic Event Boundary Detection (GEBD) is a newly introduced task that aims to detect "general" event boundaries that correspond to natural human perception. In this paper, we introduce a novel contrastive learning based approach to deal with the GEBD. Our intuition is that the feature similarity of the video snippet would significantly vary near the event boundaries, while remaining relatively the same in the remaining part of the video. In our model, Temporal Self-similarity Matrix (TSM) is utilized as an intermediate representation which takes on a role as an information bottleneck. With our model, we achieved significant performance boost compared to the given baselines. Our code is available at https://github.com/hello-jinwoo/LOVEU-CVPR2021.