# Finding Action Tubes with a Sparse-to-Dense Framework

arXiv: https://arxiv.org/abs/2008.13196 · HTML: https://mlsanity.com/paper/2008.13196/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Holistic Interaction Transformer Network for Action Detection](https://mlsanity.com/paper/2210.12686/) (2022)
- [A Survey on Deep Learning-based Spatio-temporal Action Detection](https://mlsanity.com/paper/2308.01618/) (2023)
- [Identity-aware Graph Memory Network for Action Detection](https://mlsanity.com/paper/2108.11559/) (2021)
- [CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection](https://mlsanity.com/paper/2303.16118/) (2023)

## Abstract (arXiv)

The task of spatial-temporal action detection has attracted increasing attention among researchers. Existing dominant methods solve this problem by relying on short-term information and dense serial-wise detection on each individual frames or clips. Despite their effectiveness, these methods showed inadequate use of long-term information and are prone to inefficiency. In this paper, we propose for the first time, an efficient framework that generates action tube proposals from video streams with a single forward pass in a sparse-to-dense manner. There are two key characteristics in this framework: (1) Both long-term and short-term sampled information are explicitly utilized in our spatiotemporal network, (2) A new dynamic feature sampling module (DTS) is designed to effectively approximate the tube output while keeping the system tractable. We evaluate the efficacy of our model on the UCF101-24, JHMDB-21 and UCFSports benchmark datasets, achieving promising results that are competitive to state-of-the-art methods. The proposed sparse-to-dense strategy rendered our framework about 7.6 times more efficient than the nearest competitor.