  Contrastive learning has emerged as a competitive pretraining method for
object detection. Despite this progress, there has been minimal investigation
into the robustness of contrastively pretrained detectors when faced with
domain shifts. To address this gap, we conduct an empirical study of
contrastive learning and out-of-domain object detection, studying how
contrastive view design affects robustness. In particular, we perform a case
study of the detection-focused pretext task Instance Localization (InsLoc) and
propose strategies to augment views and enhance robustness in
appearance-shifted and context-shifted scenarios. Amongst these strategies, we
propose changes to cropping such as altering the percentage used, adding IoU
constraints, and integrating saliency based object priors. We also explore the
addition of shortcut-reducing augmentations such as Poisson blending, texture
flattening, and elastic deformation. We benchmark these strategies on abstract,
weather, and context domain shifts and illustrate robust ways to combine them,
in both pretraining on single-object and multi-object image datasets. Overall,
our results and insights show how to ensure robustness through the choice of
views in contrastive learning.
