# Time and Frequency Network for Human Action Detection in Videos

arXiv: https://arxiv.org/abs/2103.04680 · HTML: https://mlsanity.com/paper/2103.04680/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks](https://mlsanity.com/paper/1711.10305/) (2017)
- [A Short Note on the Kinetics-700 Human Action Dataset](https://mlsanity.com/paper/1907.06987/) (2019)
- [Learning in the Frequency Domain](https://mlsanity.com/paper/2002.12416/) (2020)
- [Action Tubelet Detector for Spatio-Temporal Action Localization](https://mlsanity.com/paper/1705.01861/) (2017)
- [You Only Watch Once: A Unified CNN Architecture for Real-Time Spatiotemporal Action Localization](https://mlsanity.com/paper/1911.06644/) (2019)
- [Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos](https://mlsanity.com/paper/1703.10664/) (2017)
- [TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection](https://mlsanity.com/paper/1905.13417/) (2019)
- [Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos](https://mlsanity.com/paper/1608.01529/) (2016)
- [Context-Aware RCNN: A Baseline for Action Detection in Videos](https://mlsanity.com/paper/2007.09861/) (2020)

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

- [Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline](https://mlsanity.com/paper/2206.11459/) (2022)

## Abstract (arXiv)

Currently, spatiotemporal features are embraced by most deep learning approaches for human action detection in videos, however, they neglect the important features in frequency domain. In this work, we propose an end-to-end network that considers the time and frequency features simultaneously, named TFNet. TFNet holds two branches, one is time branch formed of three-dimensional convolutional neural network(3D-CNN), which takes the image sequence as input to extract time features; and the other is frequency branch, extracting frequency features through two-dimensional convolutional neural network(2D-CNN) from DCT coefficients. Finally, to obtain the action patterns, these two features are deeply fused under the attention mechanism. Experimental results on the JHMDB51-21 and UCF101-24 datasets demonstrate that our approach achieves remarkable performance for frame-mAP.