# Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement

arXiv: https://arxiv.org/abs/2205.03569 · HTML: https://mlsanity.com/paper/2205.03569/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 23 references that are in the map.
- First posted on arXiv 7 May 2022.

## Builds on (most cited first, 19 of 19)

- [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)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Beyond Short Snippets: Deep Networks for Video Classification](https://mlsanity.com/paper/1503.08909/) (2015)
- [TDN: Temporal Difference Networks for Efficient Action Recognition](https://mlsanity.com/paper/2012.10071/) (2020)
- [TEA: Temporal Excitation and Aggregation for Action Recognition](https://mlsanity.com/paper/2004.01398/) (2020)
- [STM: SpatioTemporal and Motion Encoding for Action Recognition](https://mlsanity.com/paper/1908.02486/) (2019)
- [Compressed Video Action Recognition](https://mlsanity.com/paper/1712.00636/) (2017)
- [Appearance-and-Relation Networks for Video Classification](https://mlsanity.com/paper/1711.09125/) (2017)
- [Real-time Action Recognition with Enhanced Motion Vector CNNs](https://mlsanity.com/paper/1604.07669/) (2016)
- [DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition](https://mlsanity.com/paper/1901.03460/) (2019)
- [Mimic The Raw Domain: Accelerating Action Recognition in the Compressed Domain](https://mlsanity.com/paper/1911.08206/) (2019)
- [Mobile Video Action Recognition](https://mlsanity.com/paper/1908.10155/) (2019)
- [Compressed Video Action Recognition with Refined Motion Vector](https://mlsanity.com/paper/1910.02533/) (2019)
- [Flow-Distilled IP Two-Stream Networks for Compressed Video Action Recognition](https://mlsanity.com/paper/1912.04462/) (2019)

## Built on by (most cited first, 1 of 1)

- [DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition](https://mlsanity.com/paper/2507.12426/) (2025)

## Abstract (arXiv)

Compressed video action recognition has recently drawn growing attention, since it remarkably reduces the storage and computational cost via replacing raw videos by sparsely sampled RGB frames and compressed motion cues (e.g., motion vectors and residuals). However, this task severely suffers from the coarse and noisy dynamics and the insufficient fusion of the heterogeneous RGB and motion modalities. To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net). It follows the two-stream architecture, i.e. one for the RGB modality and the other for the motion modality. Particularly, the motion stream employs a multi-scale block embedded with a denoising module to enhance representation learning. The interaction between the two streams is then strengthened by introducing the Selective Motion Complement (SMC) and Cross-Modality Augment (CMA) modules, where SMC complements the RGB modality with spatio-temporally attentive local motion features and CMA further combines the two modalities with selective feature augmentation. Extensive experiments on the UCF-101, HMDB-51 and Kinetics-400 benchmarks demonstrate the effectiveness and efficiency of MEACI-Net.