Learning latent structure for activity recognition

By Ninghang Hu, Gwenn Englebienne, Zhongyu Lou, Ben J. A. Kröse. 2014

45 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Stefan Wermter
2. German Ignacio Parisi
3. Yali Wang
4. Yu Qiao
5. Juan Carlos Niebles
6. Luiza Mici
7. Luc Van Gool
8. Wenbin Du

In Signal Processing

1. German Ignacio Parisi
2. Luiza Mici
3. Stefan Wermter
4. Yu Qiao
5. Juan Carlos Niebles
6. Yandong Wen
7. Weihong Deng
8. Zhifeng Li

Citations in the corpus

• A self-organizing neural network architecture for learning human-object interactions (2017, methodology): “We reported only the approaches that do not use groundtruth temporal segmentation of the activities into smaller atomic actions or sub-activities [61, 62].”
• A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets (2016, background): “In particular, the use of latent variables as an intermediary representation in the internal layers of the model can be a powerful tool to build discriminative models and meaningful representations [10, 34].”
• RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos (2017, background): “To improve the performance, several latent structures were proposed by learning meaningful hierarchical pose representations for action recognition [13, 22, 48].”