# Flow Dynamics Correction for Action Recognition

arXiv: https://arxiv.org/abs/2310.10059 · HTML: https://mlsanity.com/paper/2310.10059/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 43 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 26 of 26)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://mlsanity.com/paper/2303.16727/) (2023)
- [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://mlsanity.com/paper/1604.06573/) (2016)
- [ActionCLIP: A New Paradigm for Video Action Recognition](https://mlsanity.com/paper/2109.08472/) (2021)
- [Long-Term Feature Banks for Detailed Video Understanding](https://mlsanity.com/paper/1812.05038/) (2018)
- [MoViNets: Mobile Video Networks for Efficient Video Recognition](https://mlsanity.com/paper/2103.11511/) (2021)
- [VidTr: Video Transformer Without Convolutions](https://mlsanity.com/paper/2104.11746/) (2021)
- [EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow](https://mlsanity.com/paper/1501.02565/) (2015)
- [Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning](https://mlsanity.com/paper/2212.03229/) (2022)
- [Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models](https://mlsanity.com/paper/2301.00182/) (2022)
- [AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures](https://mlsanity.com/paper/1905.13209/) (2019)
- [3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition](https://mlsanity.com/paper/2303.14474/) (2023)
- [A Comparative Review of Recent Kinect-based Action Recognition Algorithms](https://mlsanity.com/paper/1906.09955/) (2019)
- [AssembleNet++: Assembling Modality Representations via Attention Connections](https://mlsanity.com/paper/2008.08072/) (2020)
- [Tensor Representations for Action Recognition](https://mlsanity.com/paper/2012.14371/) (2020)
- [Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors](https://mlsanity.com/paper/2001.04627/) (2020)
- [Fusing Higher-order Features in Graph Neural Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/2105.01563/) (2021)
- [Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition](https://mlsanity.com/paper/2210.16820/) (2022)
- [Uncertainty-DTW for Time Series and Sequences](https://mlsanity.com/paper/2211.00005/) (2022)
- [Evolving Space-Time Neural Architectures for Videos](https://mlsanity.com/paper/1811.10636/) (2018)
- [Loss Switching Fusion with Similarity Search for Video Classification](https://mlsanity.com/paper/1906.11465/) (2019)
- [High-order Tensor Pooling with Attention for Action Recognition](https://mlsanity.com/paper/2110.05216/) (2021)
- [Non-Linear Temporal Subspace Representations for Activity Recognition](https://mlsanity.com/paper/1803.11064/) (2018)
- [Analysis and Evaluation of Kinect-based Action Recognition Algorithms](https://mlsanity.com/paper/2112.08626/) (2021)

## Built on by (most cited first, 12 of 12)

- [Advancing Video Anomaly Detection: A Concise Review and a New Dataset](https://mlsanity.com/paper/2402.04857/) (2024)
- [Do Language Models Understand Time?](https://mlsanity.com/paper/2412.13845/) (2024)
- [Taylor Videos for Action Recognition](https://mlsanity.com/paper/2402.03019/) (2024)
- [TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps](https://mlsanity.com/paper/2409.14543/) (2024)
- [Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment](https://mlsanity.com/paper/2402.04599/) (2024)
- [EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation](https://mlsanity.com/paper/2506.03512/) (2025)
- [When Spatial meets Temporal in Action Recognition](https://mlsanity.com/paper/2411.15284/) (2024)
- [Feature Hallucination for Self-supervised Action Recognition](https://mlsanity.com/paper/2506.20342/) (2025)
- [Evolving Skeletons: Motion Dynamics in Action Recognition](https://mlsanity.com/paper/2501.02593/) (2025)
- [Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection](https://mlsanity.com/paper/2605.08651/) (2026)
- [Trust-Aware Joint Feature-Prediction Discrepancy for Robust Domain Adaptation](https://mlsanity.com/paper/2605.25119/) (2026)
- [Paving the Way for Point Cloud Video Representation Learning Using A PDE Model](https://mlsanity.com/paper/2606.01604/) (2026)

## Abstract (arXiv)

Various research studies indicate that action recognition performance highly depends on the types of motions being extracted and how accurate the human actions are represented. In this paper, we investigate different optical flow, and features extracted from these optical flow that capturing both short-term and long-term motion dynamics. We perform power normalization on the magnitude component of optical flow for flow dynamics correction to boost subtle or dampen sudden motions. We show that existing action recognition models which rely on optical flow are able to get performance boosted with our corrected optical flow. To further improve performance, we integrate our corrected flow dynamics into popular models through a simple hallucination step by selecting only the best performing optical flow features, and we show that by 'translating' the CNN feature maps into these optical flow features with different scales of motions leads to the new state-of-the-art performance on several benchmarks including HMDB-51, YUP++, fine-grained action recognition on MPII Cooking Activities, and large-scale Charades.