Deep convolutional networks for semantic image segmentation typically require
large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training.
To reduce annotation efforts, self-supervised semantic segmentation is recently
proposed to pre-train a network without any human-provided labels. The key of
this new form of learning is to design a proxy task (e.g. image colorization),
from which a discriminative loss can be formulated on unlabeled data. Many
proxy tasks, however, lack the critical supervision signals that could induce
discriminative representation for the target image segmentation task. Thus
self-supervision's performance is still far from that of supervised
pre-training. In this study, we overcome this limitation by incorporating a
"mix-and-match" (M&M) tuning stage in the self-supervision pipeline. The
proposed approach is readily pluggable to many self-supervision methods and
does not use more annotated samples than the original process. Yet, it is
capable of boosting the performance of target image segmentation task to
surpass fully-supervised pre-trained counterpart. The improvement is made
possible by better harnessing the limited pixel-wise annotations in the target
dataset. Specifically, we first introduce the "mix" stage, which sparsely
samples and mixes patches from the target set to reflect rich and diverse local
patch statistics of target images. A "match" stage then forms a class-wise
connected graph, which can be used to derive a strong triplet-based
discriminative loss for fine-tuning the network. Our paradigm follows the
standard practice in existing self-supervised studies and no extra data or
label is required. With the proposed M&M approach, for the first time, a
self-supervision method can achieve comparable or even better performance
compared to its ImageNet pre-trained counterpart on both PASCAL VOC2012 dataset
and CityScapes dataset.

该研究提出了一种自我监督学习方法，通过引入 “混合和匹配” 步骤来提高语义图像分割任务的性能，该方法无需更多的数据和标签，并且在很多方面与有监督的预训练方法可以媲美甚至超过其性能。