  Image resizing operation is a fundamental preprocessing module in modern
computer vision. Throughout the deep learning revolution, researchers have
overlooked the potential of alternative resizing methods beyond the commonly
used resizers that are readily available, such as nearest-neighbors, bilinear,
and bicubic. The key question of our interest is whether the front-end resizer
affects the performance of deep vision models? In this paper, we present an
extremely lightweight multilayer Laplacian resizer with only a handful of
trainable parameters, dubbed MULLER resizer. MULLER has a bandpass nature in
that it learns to boost details in certain frequency subbands that benefit the
downstream recognition models. We show that MULLER can be easily plugged into
various training pipelines, and it effectively boosts the performance of the
underlying vision task with little to no extra cost. Specifically, we select a
state-of-the-art vision Transformer, MaxViT, as the baseline, and show that, if
trained with MULLER, MaxViT gains up to 0.6% top-1 accuracy, and meanwhile
enjoys 36% inference cost saving to achieve similar top-1 accuracy on
ImageNet-1k, as compared to the standard training scheme. Notably, MULLER's
performance also scales with model size and training data size such as
ImageNet-21k and JFT, and it is widely applicable to multiple vision tasks,
including image classification, object detection and segmentation, as well as
image quality assessment.
