  Achieving constant accuracy in object detection is challenging due to the
inherent variability of object sizes. One effective approach to this problem
involves optimizing input resolution, referred to as a multi-resolution
strategy. Previous approaches to resolution optimization have often been based
on pre-defined resolutions with manual selection. However, there is a lack of
study on run-time resolution optimization for existing architectures. This
paper introduces DyRA, a dynamic resolution adjustment network providing an
image-specific scale factor for existing detectors. This network is co-trained
with detectors utilizing specially designed loss functions, namely
ParetoScaleLoss and BalanceLoss. ParetoScaleLoss determines an adaptive scale
factor for robustness, while BalanceLoss optimizes overall scale factors
according to the localization performance of the detector. The loss function is
devised to minimize the accuracy drop across contrasting objectives of
different-sized objects for scaling. Our proposed network can improve accuracy
across various models, including RetinaNet, Faster-RCNN, FCOS, DINO, and
H-Deformable-DETR. The code is available at
https://github.com/DaEunFullGrace/DyRA.git.
