This paper briefly introduces the solutions developed by our team, HFUT-VUT,
for Track 1 of self-supervised heart rate measurement in the 3rd Vision-based
Remote Physiological Signal Sensing (RePSS) Challenge hosted at IJCAI 2024. The
goal is to develop a self-supervised learning algorithm for heart rate (HR)
estimation using unlabeled facial videos. To tackle this task, we present two
self-supervised HR estimation solutions that integrate spatial-temporal
modeling and contrastive learning, respectively. Specifically, we first propose
a non-end-to-end self-supervised HR measurement framework based on
spatial-temporal modeling, which can effectively capture subtle rPPG clues and
leverage the inherent bandwidth and periodicity characteristics of rPPG to
constrain the model. Meanwhile, we employ an excellent end-to-end solution
based on contrastive learning, aiming to generalize across different scenarios
from complementary perspectives. Finally, we combine the strengths of the above
solutions through an ensemble strategy to generate the final predictions,
leading to a more accurate HR estimation. As a result, our solutions achieved a
remarkable RMSE score of 8.85277 on the test dataset, securing \textbf{2nd
place} in Track 1 of the challenge.

我们的研究论文介绍了关于自监督心率测量的解决方案，其中我们提出了基于空间 - 时间建模和对比学习的自监督心率估计算法，并通过组合策略获得更准确的心率估计，最终在挑战赛中获得第二名。

联合时空建模和对比学习的自监督心率测量

Joint Spatial-Temporal Modeling and Contrastive Learning for  Self-supervised Heart Rate Measurement

Remote photoplethysmography (rPPG), which aims at measuring heart activities
without any contact, has great potential in many applications (e.g., remote
healthcare). Existing end-to-end rPPG and heart rate (HR) measurement methods
from facial videos are vulnerable to the less-constrained scenarios (e.g., with
head movement and bad illumination). In this letter, we explore the reason why
existing end-to-end networks perform poorly in challenging conditions and
establish a strong end-to-end baseline (AutoHR) for remote HR measurement with
neural architecture search (NAS). The proposed method includes three parts: 1)
a powerful searched backbone with novel Temporal Difference Convolution (TDC),
intending to capture intrinsic rPPG-aware clues between frames; 2) a hybrid
loss function considering constraints from both time and frequency domains; and
3) spatio-temporal data augmentation strategies for better representation
learning. Comprehensive experiments are performed on three benchmark datasets
to show our superior performance on both intra- and cross-dataset testing.

本文使用神经架构搜索技术，提出一种基于 AutoHR 的强有力的远程心率测量终端到终端方法，通过实验在三个基准数据集上表明了在信息丰富的 rPPG-scenarios 中的效果。