# Multimodal Multipart Learning for Action Recognition in Depth Videos

arXiv: https://arxiv.org/abs/1507.08761 · HTML: https://mlsanity.com/paper/1507.08761/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2015.

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

- [Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition](https://mlsanity.com/paper/2110.13008/) (2021)
- [ViLP: Knowledge Exploration using Vision, Language, and Pose Embeddings for Video Action Recognition](https://mlsanity.com/paper/2308.03908/) (2023)
- [Learning Scene Flow With Skeleton Guidance For 3D Action Recognition](https://mlsanity.com/paper/2306.13285/) (2023)
- [Multi-Modal Graph Convolutional Network with Sinusoidal Encoding for Robust Human Action Segmentation](https://mlsanity.com/paper/2507.00752/) (2025)

## Abstract (arXiv)

The articulated and complex nature of human actions makes the task of action recognition difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and analyzing the actions based on these partial descriptors. We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts. To represent dynamics and appearance of parts, we employ a heterogeneous set of depth and skeleton based features. The proper structure of multimodal multipart features are formulated into the learning framework via the proposed hierarchical mixed norm, to regularize the structured features of each part and to apply sparsity between them, in favor of a group feature selection. Our experimental results expose the effectiveness of the proposed learning method in which it outperforms other methods in all three tested datasets while saturating one of them by achieving perfect accuracy.