Pixel-Wise Contrastive Distillation

TL;DR

This paper introduces Pixel-Wise Contrastive Distillation (PCD), a self-supervised framework for dense prediction tasks that improves knowledge transfer at the pixel level using novel reshaping and attention mechanisms.

Contribution

The paper proposes a new pixel-level distillation method with a SpatialAdaptor and multi-head self-attention, enhancing dense prediction performance.

Findings

Outperforms previous self-supervised distillation methods on dense tasks

ResNet-18-FPN distilled by PCD achieves 37.4 AP_bbox on COCO

ResNet-18-FPN distilled by PCD achieves 34.0 AP_mask on COCO

Abstract

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 \textbf{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$^\text{bbox}$ and $34.0$ AP$^\text{mask}$ 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.