# What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection

arXiv: https://arxiv.org/abs/2404.18935 · HTML: https://mlsanity.com/paper/2404.18935/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 46 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [Temporal Segment Networks for Action Recognition in Videos](https://mlsanity.com/paper/1705.02953/) (2017)
- [ActBERT: Learning Global-Local Video-Text Representations](https://mlsanity.com/paper/2011.07231/) (2020)
- [ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias](https://mlsanity.com/paper/2106.03348/) (2021)
- [Rethinking the Faster R-CNN Architecture for Temporal Action Localization](https://mlsanity.com/paper/1804.07667/) (2018)
- [MoViNets: Mobile Video Networks for Efficient Video Recognition](https://mlsanity.com/paper/2103.11511/) (2021)
- [MotionSqueeze: Neural Motion Feature Learning for Video Understanding](https://mlsanity.com/paper/2007.09933/) (2020)
- [Generic Event Boundary Detection: A Benchmark for Event Segmentation](https://mlsanity.com/paper/2101.10511/) (2021)
- [Intra- and Inter-Action Understanding via Temporal Action Parsing](https://mlsanity.com/paper/2005.10229/) (2020)
- [Learning Video Representations from Correspondence Proposals](https://mlsanity.com/paper/1905.07853/) (2019)
- [UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection](https://mlsanity.com/paper/2111.14799/) (2021)
- [Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection](https://mlsanity.com/paper/2112.04771/) (2021)
- [GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval](https://mlsanity.com/paper/2204.00486/) (2022)
- [Proposal Relation Network for Temporal Action Detection](https://mlsanity.com/paper/2106.11812/) (2021)
- [End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection](https://mlsanity.com/paper/2203.15336/) (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)
- [Towards Robust Video Object Segmentation with Adaptive Object Calibration](https://mlsanity.com/paper/2207.00887/) (2022)
- [Structured Context Transformer for Generic Event Boundary Detection](https://mlsanity.com/paper/2206.02985/) (2022)
- [CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation](https://mlsanity.com/paper/2109.15170/) (2021)
- [Exploring Temporal Granularity in Self-Supervised Video Representation Learning](https://mlsanity.com/paper/2112.04480/) (2021)
- [SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection](https://mlsanity.com/paper/2206.12634/) (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)
- [Online Generic Event Boundary Detection](https://mlsanity.com/paper/2510.06855/) (2025)
- [FocusGraph: Graph-Structured Frame Selection for Embodied Long Video Question Answering](https://mlsanity.com/paper/2603.04349/) (2026)
- [Training-Free Temporal Abstraction for General Video Understanding](https://mlsanity.com/paper/2608.27929/) (2026)

## Abstract (arXiv)

Generic Event Boundary Detection (GEBD) task aims to recognize generic, taxonomy-free boundaries that segment a video into meaningful events. Current methods typically involve a neural model trained on a large volume of data, demanding substantial computational power and storage space. We explore two pivotal questions pertaining to GEBD: Can non-parametric algorithms outperform unsupervised neural methods? Does motion information alone suffice for high performance? This inquiry drives us to algorithmically harness motion cues for identifying generic event boundaries in videos. In this work, we propose FlowGEBD, a non-parametric, unsupervised technique for GEBD. Our approach entails two algorithms utilizing optical flow: (i) Pixel Tracking and (ii) Flow Normalization. By conducting thorough experimentation on the challenging Kinetics-GEBD and TAPOS datasets, our results establish FlowGEBD as the new state-of-the-art (SOTA) among unsupervised methods. FlowGEBD exceeds the neural models on the Kinetics-GEBD dataset by obtaining an F1@0.05 score of 0.713 with an absolute gain of 31.7% compared to the unsupervised baseline and achieves an average F1 score of 0.623 on the TAPOS validation dataset.