  Understanding of human visual perception has historically inspired the design
of computer vision architectures. As an example, perception occurs at different
scales both spatially and temporally, suggesting that the extraction of salient
visual information may be made more effective by paying attention to specific
features at varying scales. Visual changes in the body due to physiological
processes also occur at different scales and with modality-specific
characteristic properties. Inspired by this, we present BigSmall, an efficient
architecture for physiological and behavioral measurement. We present the first
joint camera-based facial action, cardiac, and pulmonary measurement model. We
propose a multi-branch network with wrapping temporal shift modules that yields
both accuracy and efficiency gains. We observe that fusing low-level features
leads to suboptimal performance, but that fusing high level features enables
efficiency gains with negligible loss in accuracy. Experimental results
demonstrate that BigSmall significantly reduces the computational costs.
Furthermore, compared to existing task-specific models, BigSmall achieves
comparable or better results on multiple physiological measurement tasks
simultaneously with a unified model.
