EnsAug: Augmentation-Driven Ensembles for Human Motion Sequence Analysis

TL;DR

This paper introduces EnsAug, a novel ensemble training approach that uses distinct geometric augmentations for each model to improve human motion sequence analysis, achieving state-of-the-art accuracy.

Contribution

The paper proposes a new ensemble training paradigm that leverages augmentation-driven diversity, outperforming traditional single-model training on augmented datasets.

Findings

Outperforms standard single-model training on augmented data

Achieves state-of-the-art accuracy on multiple human motion datasets

Provides a modular and efficient ensemble training framework

Abstract

Data augmentation is a crucial technique for training robust deep learning models for human motion, where annotated datasets are often scarce. However, generic augmentation methods often ignore the underlying geometric and kinematic constraints of the human body, risking the generation of unrealistic motion patterns that can degrade model performance. Furthermore, the conventional approach of training a single generalist model on a dataset expanded with a mixture of all available transformations does not fully exploit the unique learning signals provided by each distinct augmentation type. We challenge this convention by introducing a novel training paradigm, EnsAug, that strategically uses augmentation to foster model diversity within an ensemble. Our method involves training an ensemble of specialists, where each model learns from the original dataset augmented by only a single, distinct geometric transformation. Experiments on sign language and human activity recognition benchmarks demonstrate that our diversified ensemble methodology significantly outperforms the standard practice of training one model on a combined augmented dataset and achieves state-of-the-art accuracy on two sign language and one human activity recognition dataset while offering greater modularity and efficiency. Our primary contribution is the empirical validation of this training strategy, establishing an effective baseline for leveraging data augmentation in skeletal motion analysis.