We present a simple but effective pixel-level self-supervised distillation framework friendly to dense prediction tasks. Our method, called Pixel-Wise Contrastive Distillation (PCD), distills knowledge by attracting the corresponding pixels from student's and teacher's output feature maps.

PCD includes a novel design called SpatialAdaptor which "reshapes" a part of the teacher network while preserving the distribution of its output features. Our ablation experiments suggest that this reshaping behavior enables more informative pixel-to-pixel distillation.

Moreover, we utilize a plug-in multi-head self-attention module that explicitly relates the pixels of student's feature maps to enhance the effective receptive field, leading to a more competitive student. PCD outperforms previous self-supervised distillation methods on various dense prediction tasks. A backbone of \mbox{ResNet-18-FPN} distilled by PCD achieves $37.4$ AP$^bbox$ and $34.0$ AP$^mask$ on COCO dataset using the detector of \mbox{Mask R-CNN}.

We hope our study will inspire future research on how to pre-train a small model friendly to dense prediction tasks in a self-supervised fashion.