  Scene change detection (SCD), a crucial perception task, identifies changes
by comparing scenes captured at different times. SCD is challenging due to
noisy changes in illumination, seasonal variations, and perspective differences
across a pair of views. Deep neural network based solutions require a large
quantity of annotated data which is tedious and expensive to obtain. On the
other hand, transfer learning from large datasets induces domain shift. To
address these challenges, we propose a novel \textit{Differencing
self-supervised pretraining (DSP)} method that uses feature differencing to
learn discriminatory representations corresponding to the changed regions while
simultaneously tackling the noisy changes by enforcing temporal invariance
across views. Our experimental results on SCD datasets demonstrate the
effectiveness of our method, specifically to differences in camera viewpoints
and lighting conditions. Compared against the self-supervised Barlow Twins and
the standard ImageNet pretraining that uses more than a million additional
labeled images, DSP can surpass it without using any additional data. Our
results also demonstrate the robustness of DSP to natural corruptions,
distribution shift, and learning under limited labeled data.
