# Two-stream Flow-guided Convolutional Attention Networks for Action Recognition

arXiv: https://arxiv.org/abs/1708.09268 · HTML: https://mlsanity.com/paper/1708.09268/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [Aligning Correlation Information for Domain Adaptation in Action Recognition](https://mlsanity.com/paper/2107.04932/) (2021)
- [Deep video representation learning: a survey](https://mlsanity.com/paper/2405.06574/) (2024)
- [InFi: End-to-End Learning to Filter Input for Resource-Efficiency in Mobile-Centric Inference](https://mlsanity.com/paper/2209.13873/) (2022)

## Abstract (arXiv)

This paper proposes a two-stream flow-guided convolutional attention networks for action recognition in videos. The central idea is that optical flows, when properly compensated for the camera motion, can be used to guide attention to the human foreground. We thus develop cross-link layers from the temporal network (trained on flows) to the spatial network (trained on RGB frames). These cross-link layers guide the spatial-stream to pay more attention to the human foreground areas and be less affected by background clutter. We obtain promising performances with our approach on the UCF101, HMDB51 and Hollywood2 datasets.