To help contextualize progress on ImageNet and provide a more meaningful evaluation for today's state-of-the-art models, we manually review and categorize every remaining mistake that a few top models make in order to provide insight into the long-tail of errors on one of the most benchmarked datasets in computer vision.
We also explore the connections between function frequency and image frequency and find that spectral bias is sensitive to the low frequencies prevalent in natural images.
We show that passing input points through a simple Fourier feature mapping enables a multilayer perceptron (MLP) to learn high-frequency functions in low-dimensional problem domains.
By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition.
We consider the problem of smartphone video-based heart rate estimation, which typically relies on measuring the green color intensity of the user's skin.
Medical Physics Image and Video Processing