# Hallucinating Optical Flow Features for Video Classification

arXiv: https://arxiv.org/abs/1905.11799 · HTML: https://mlsanity.com/paper/1905.11799/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2019.

## Built on by (most cited first, 5 of 5)

- [Advances and Challenges in Deep Lip Reading](https://mlsanity.com/paper/2110.07879/) (2021)
- [A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection](https://mlsanity.com/paper/2404.01643/) (2024)
- [Feature Hallucination for Self-supervised Action Recognition](https://mlsanity.com/paper/2506.20342/) (2025)
- [Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis](https://mlsanity.com/paper/2502.07277/) (2025)
- [Two-Stream temporal transformer for video action classification](https://mlsanity.com/paper/2601.14086/) (2026)

## Abstract (arXiv)

Appearance and motion are two key components to depict and characterize the video content. Currently, the two-stream models have achieved state-of-the-art performances on video classification. However, extracting motion information, specifically in the form of optical flow features, is extremely computationally expensive, especially for large-scale video classification. In this paper, we propose a motion hallucination network, namely MoNet, to imagine the optical flow features from the appearance features, with no reliance on the optical flow computation. Specifically, MoNet models the temporal relationships of the appearance features and exploits the contextual relationships of the optical flow features with concurrent connections. Extensive experimental results demonstrate that the proposed MoNet can effectively and efficiently hallucinate the optical flow features, which together with the appearance features consistently improve the video classification performances. Moreover, MoNet can help cutting down almost a half of computational and data-storage burdens for the two-stream video classification. Our code is available at: https://github.com/YongyiTang92/MoNet-Features.