# Hierarchical Action Classification with Network Pruning

arXiv: https://arxiv.org/abs/2007.15244 · HTML: https://mlsanity.com/paper/2007.15244/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [STAR-Transformer: A Spatio-temporal Cross Attention Transformer for Human Action Recognition](https://mlsanity.com/paper/2210.07503/) (2022)
- [Transformers in Action Recognition: A Review on Temporal Modeling](https://mlsanity.com/paper/2302.01921/) (2022)
- [Cross-view Action Recognition via Contrastive View-invariant Representation](https://mlsanity.com/paper/2305.01733/) (2023)
- [Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos](https://mlsanity.com/paper/2204.13548/) (2022)
- [Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition](https://mlsanity.com/paper/2309.05032/) (2023)
- [LS-HAR: Language Supervised Human Action Recognition with Salient Fusion, Construction Sites as a Use-Case](https://mlsanity.com/paper/2410.01962/) (2024)

## Abstract (arXiv)

Research on human action classification has made significant progresses in the past few years. Most deep learning methods focus on improving performance by adding more network components. We propose, however, to better utilize auxiliary mechanisms, including hierarchical classification, network pruning, and skeleton-based preprocessing, to boost the model robustness and performance. We test the effectiveness of our method on four commonly used testing datasets: NTU RGB+D 60, NTU RGB+D 120, Northwestern-UCLA Multiview Action 3D, and UTD Multimodal Human Action Dataset. Our experiments show that our method can achieve either comparable or better performance on all four datasets. In particular, our method sets up a new baseline for NTU 120, the largest dataset among the four. We also analyze our method with extensive comparisons and ablation studies.