The utilization of remote photoplethysmography (rPPG) technology has gained attention in recent years due to its ability to extract blood volume pulse (BVP) from facial videos, making it accessible for various applications such as health monitoring and emotional analysis. However, the BVP signal is susceptible to complex environmental changes or individual differences, causing existing methods to struggle in generalizing for unseen domains. This paper addresses the domain shift problem in rPPG measurement and shows that most domain generalization methods fail to work well in this problem due to ambiguous instance-specific differences. To address this, the paper proposes a novel approach called Hierarchical Style-aware Representation Disentangling (HSRD). HSRD improves generalization capacity by separating domain-invariant and instance-specific feature space during training, which increases the robustness of out-of-distribution samples during inference. This work presents state-of-the-art performance against several methods in both cross and intra-dataset settings.

The rapid advancement of large language models (LLMs) such as ChatGPT makes LLM-based academic tools possible. However, little research has empirically evaluated how scholars perform different types of academic tasks with LLMs. Through an empirical study followed by a semi-structured interview, we assessed 48 early-stage scholars’ performance in conducting core academic activities (i.e., paper reading and literature reviews) under different levels of time pressure. Before conducting the tasks, participants received different training programs regarding the limitations and capabilities of the LLMs. After completing the tasks, participants completed an interview. Quantitative data regarding the influence of time pressure, task type, and training program on participants’ performance in academic tasks was analyzed. Semi-structured interviews provided additional information on the influential factors of task performance, participants’ perceptions of LLMs, and concerns about integrating LLMs into academic workflows. The findings can guide more appropriate usage and design of LLM-based tools in assisting academic work.

Remote photoplethysmography (rPPG) has been widely applied to measure heart rate from face videos. To increase the generalizability of the algorithms, domain generalization (DG) attracted increasing attention in rPPG. However, when rPPG is extended to simultaneously measure more vital signs (e.g., respiration and blood oxygen saturation), achieving generalizability brings new challenges. Although partial features shared among different physiological signals can benefit multi-task learning, the sparse and imbalanced target label space brings the seesaw effect over task-specific feature learning. To resolve this problem, we designed an end-to-end Mixture of Low-rank Experts for multi-task remote Physiological measurement (PhysMLE), which is based on multiple low-rank experts with a novel router mechanism, thereby enabling the model to adeptly handle both specifications and correlations within tasks. Additionally, we introduced prior knowledge from physiology among tasks to overcome the imbalance of label space under real-world multi-task physiological measurement. For fair and comprehensive evaluations, this paper proposed a large-scale multi-task generalization benchmark, named Multi-Source Synsemantic Domain Generalization (MSSDG) protocol. Extensive experiments with MSSDG and intra-dataset have shown the effectiveness and efficiency of PhysMLE. In addition, a new dataset was collected and made publicly available to meet the needs of the MSSDG. The code is available at https://github.com/WJULYW/PhysMLE.