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Understanding of human visual perception has historically inspired the designof computer vision architectures. As an example, perception occurs at differentscales both spatially and temporally, suggesting that the extraction of salientvisual information may be made more effective by paying attention to specificfeatures at varying scales. Visual changes in the body due to physiologicalprocesses also occur at different scales and with modality-specificcharacteristic properties. Inspired by this, we present BigSmall, an efficientarchitecture for physiological and behavioral measurement. We present the firstjoint camera-based facial action, cardiac, and pulmonary measurement model. Wepropose a multi-branch network with wrapping temporal shift modules that yieldsboth accuracy and efficiency gains. We observe that fusing low-level featuresleads to suboptimal performance, but that fusing high level features enablesefficiency gains with negligible loss in accuracy. Experimental resultsdemonstrate that BigSmall significantly reduces the computational costs.Furthermore, compared to existing task-specific models, BigSmall achievescomparable or better results on multiple physiological measurement taskssimultaneously with a unified model.

BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements