Multimodal Contrastive Learning with Hard Negative Sampling for Human Activity Recognition

TL;DR

This paper introduces a novel multimodal contrastive learning approach with hard negative sampling for human activity recognition, improving feature representation and outperforming existing methods on benchmark datasets.

Contribution

It proposes a hard negative sampling method tailored for multimodal HAR, enhancing contrastive learning effectiveness with limited annotated data.

Findings

Outperforms state-of-the-art on UTD-MHAD dataset

Effective in limited data scenarios

Robust feature learning across modalities

Abstract

Human Activity Recognition (HAR) systems have been extensively studied by the vision and ubiquitous computing communities due to their practical applications in daily life, such as smart homes, surveillance, and health monitoring. Typically, this process is supervised in nature and the development of such systems requires access to large quantities of annotated data. However, the higher costs and challenges associated with obtaining good quality annotations have rendered the application of self-supervised methods an attractive option and contrastive learning comprises one such method. However, a major component of successful contrastive learning is the selection of good positive and negative samples. Although positive samples are directly obtainable, sampling good negative samples remain a challenge. As human activities can be recorded by several modalities like camera and IMU sensors, we propose a hard negative sampling method for multimodal HAR with a hard negative sampling loss for skeleton and IMU data pairs. We exploit hard negatives that have different labels from the anchor but are projected nearby in the latent space using an adjustable concentration parameter. Through extensive experiments on two benchmark datasets: UTD-MHAD and MMAct, we demonstrate the robustness of our approach forlearning strong feature representation for HAR tasks, and on the limited data setting. We further show that our model outperforms all other state-of-the-art methods for UTD-MHAD dataset, and self-supervised methods for MMAct: Cross session, even when uni-modal data are used during downstream activity recognition.