  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.
