# Actionness Estimation Using Hybrid Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1604.07279 · HTML: https://mlsanity.com/paper/1604.07279/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2016.

## Built on by (most cited first, 15 of 15)

- [MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions](https://mlsanity.com/paper/2105.07404/) (2021)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [STMixer: A One-Stage Sparse Action Detector](https://mlsanity.com/paper/2303.15879/) (2023)
- [Weakly Supervised Action Selection Learning in Video](https://mlsanity.com/paper/2105.02439/) (2021)
- [Action Detection via an Image Diffusion Process](https://mlsanity.com/paper/2404.01051/) (2024)
- [Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks](https://mlsanity.com/paper/2209.09393/) (2022)
- [A Survey on Deep Learning-based Spatio-temporal Action Detection](https://mlsanity.com/paper/2308.01618/) (2023)
- [Selective Feature Compression for Efficient Activity Recognition Inference](https://mlsanity.com/paper/2104.00179/) (2021)
- [CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection](https://mlsanity.com/paper/2303.16118/) (2023)
- [Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline](https://mlsanity.com/paper/2206.11459/) (2022)
- [Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos](https://mlsanity.com/paper/2110.09110/) (2021)
- [Point3D: tracking actions as moving points with 3D CNNs](https://mlsanity.com/paper/2203.10584/) (2022)
- [STMixer: A One-Stage Sparse Action Detector](https://mlsanity.com/paper/2404.09842/) (2024)
- [Dual Guidance Semi-Supervised Action Detection](https://mlsanity.com/paper/2507.21247/) (2025)
- [SAGE: Synchronized Action-Gaze Recognition and Anticipation for Human Behavior Understanding](https://mlsanity.com/paper/2607.04017/) (2026)

## Abstract (arXiv)

Actionness was introduced to quantify the likelihood of containing a generic action instance at a specific location. Accurate and efficient estimation of actionness is important in video analysis and may benefit other relevant tasks such as action recognition and action detection. This paper presents a new deep architecture for actionness estimation, called hybrid fully convolutional network (H-FCN), which is composed of appearance FCN (A-FCN) and motion FCN (M-FCN). These two FCNs leverage the strong capacity of deep models to estimate actionness maps from the perspectives of static appearance and dynamic motion, respectively. In addition, the fully convolutional nature of H-FCN allows it to efficiently process videos with arbitrary sizes. Experiments are conducted on the challenging datasets of Stanford40, UCF Sports, and JHMDB to verify the effectiveness of H-FCN on actionness estimation, which demonstrate that our method achieves superior performance to previous ones. Moreover, we apply the estimated actionness maps on action proposal generation and action detection. Our actionness maps advance the current state-of-the-art performance of these tasks substantially.