Self-Supervised Learning from Non-Object Centric Images with a Geometric Transformation Sensitive Architecture

Most invariance-based self-supervised methods rely on single object-centric images (e.g., ImageNet images) for pretraining, learning invariant representations from geometric transformations. However, when images are not object-centric, the semantics of the image can be significantly altered due to cropping. Furthermore, as the model learns geometrically insensitive features, it may struggle to capture location information. For this reason, we propose a Geometric Transformation Sensitive Architecture that learns features sensitive to geometric transformations, specifically four-fold rotation, random crop, and multi-crop. Our method encourages the student to learn sensitive features by using targets that are sensitive to those transforms via pooling and rotating of the teacher feature map and predicting rotation. Additionally, since training insensitively to multi-crop can capture long-term dependencies, we use patch correspondence loss to train the model sensitively while capturing long-term dependencies. Our approach demonstrates improved performance when using non-object-centric images as pretraining data compared to other methods that learn geometric transformation-insensitive representations. We surpass the DINO[\citet{caron2021emerging}] baseline in tasks including image classification, semantic segmentation, detection, and instance segmentation with improvements of 6.1 $Acc$, 3.3 $mIoU$, 3.4 $AP^b$, and 2.7 $AP^m$. Code and pretrained models are publicly available at: