This work explores the use of spatial context as a source of free and
plentiful supervisory signal for training a rich visual representation. Given
only a large, unlabeled image collection, we extract random pairs of patches
from each image and train a convolutional neural net to predict the position of
the second patch relative to the first. We argue that doing well on this task
requires the model to learn to recognize objects and their parts. We
demonstrate that the feature representation learned using this within-image
context indeed captures visual similarity across images. For example, this
representation allows us to perform unsupervised visual discovery of objects
like cats, people, and even birds from the Pascal VOC 2011 detection dataset.
Furthermore, we show that the learned ConvNet can be used in the R-CNN
framework and provides a significant boost over a randomly-initialized ConvNet,
resulting in state-of-the-art performance among algorithms which use only
Pascal-provided training set annotations.

该研究探讨了利用空间上下文作为丰富的视觉表征的监督信号的方法。通过从每个图像中提取随机配对的路径来训练卷积神经网络，用于预测第二个路径相对于第一个路径的位置，从而实现对目标的识别，并证明该方法在性能上优于其他算法。