Contrastive self-supervised learning has outperformed supervised pretraining
on many downstream tasks like segmentation and object detection. However,
current methods are still primarily applied to curated datasets like ImageNet.
In this paper, we first study how biases in the dataset affect existing
methods. Our results show that current contrastive approaches work surprisingly
well across: (i) object- versus scene-centric, (ii) uniform versus long-tailed
and (iii) general versus domain-specific datasets. Second, given the generality
of the approach, we try to realize further gains with minor modifications. We
show that learning additional invariances -- through the use of multi-scale
cropping, stronger augmentations and nearest neighbors -- improves the
representations. Finally, we observe that MoCo learns spatially structured
representations when trained with a multi-crop strategy. The representations
can be used for semantic segment retrieval and video instance segmentation
without finetuning. Moreover, the results are on par with specialized models.
We hope this work will serve as a useful study for other researchers. The code
and models are available at
this https URL

本文将需要自我学习来改进现有模型的数据集偏差性进行了深入研究，并成功地在不同类型数据集上改善了模型，同时介绍了一种具有多尺度裁剪、强数据增强和近邻策略的改进自监督学习方法，并通过 MoCo 模型在语义分割和视频实例分割任务中实现了优秀的效果。