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Attention is a powerful concept in computer vision. End-to-end networks thatlearn to focus selectively on regions of an image or video often performstrongly. However, other image regions, while not necessarily containing thesignal of interest, may contain useful context. We present an approach thatexploits the idea that statistics of noise may be shared between the regionsthat contain the signal of interest and those that do not. Our technique usesthe inverse of an attention mask to generate a noise estimate that is then usedto denoise temporal observations. We apply this to the task of camera-basedphysiological measurement. A convolutional attention network is used to learnwhich regions of a video contain the physiological signal and generate apreliminary estimate. A noise estimate is obtained by using the pixelintensities in the inverse regions of the learned attention mask, this in turnis used to refine the estimate of the physiological signal. We performexperiments on two large benchmark datasets and show that this approachproduces state-of-the-art results, increasing the signal-to-noise ratio by upto 5.8 dB, reducing heart rate and breathing rate estimation error by as muchas 30%, recovering subtle pulse waveform dynamics, and generalizing from RGB toNIR videos without retraining.

The Benefit of Distraction: Denoising Remote Vitals Measurements using Inverse Attention