In this work we present point-level region contrast, a self-supervised
pre-training approach for the task of object detection. This approach is
motivated by the two key factors in detection: localization and recognition.
While accurate localization favors models that operate at the pixel- or
point-level, correct recognition typically relies on a more holistic,
region-level view of objects. Incorporating this perspective in pre-training,
our approach performs contrastive learning by directly sampling individual
point pairs from different regions. Compared to an aggregated representation
per region, our approach is more robust to the change in input region quality,
and further enables us to implicitly improve initial region assignments via
online knowledge distillation during training. Both advantages are important
when dealing with imperfect regions encountered in the unsupervised setting.
Experiments show point-level region contrast improves on state-of-the-art
pre-training methods for object detection and segmentation across multiple
tasks and datasets, and we provide extensive ablation studies and
visualizations to aid understanding. Code will be made available.

介绍了基于自监督学习的点级区域对比法，该方法用于目标检测预训练，提高了模型在像素或点级别的定位和物体区域整体性识别的表现，通过直接从不同区域中采样个体点对执行对比学习，增强了模型对输入区域质量变化的鲁棒性，同时通过在线知识蒸馏隐式地提高了初始区域分配的质量。在多个任务和数据集上的实验表明，这种方法提高了目标检测和分割的预训练方法的性能。