Is strong supervision necessary for learning a good visual representation? Do we really need millions of semantically-labeled images to train a ConvNet? In this paper, we present a simple yet surprisingly powerful approach for unsupervised learning of ConvNets. Specifically, we use hundreds of thousands of unlabeled videos from the web to learn visual representations. Our key idea is that we track millions of patches in these videos. Visual tracking provides the key supervision. That is, two patches connected by a track should have similar visual representation in deep feature space since they probably belong to same object or object part. We design a Siamese-triplet network with a ranking loss function to train this ConvNet representation. Without using a single image from ImageNet, just using 100K unlabeled videos and the VOC 2012 dataset, we train an ensemble of unsupervised networks that achieves 52% mAP (no bounding box regression). This performance comes tantalizingly close to its ImageNet-supervised counterpart, an ensemble which achieves a mAP of 54.4%. We also show that our unsupervised network can perform competitive in other tasks such as surface-normal estimation.

本文介绍了一种使用大量未标记数据进行无监督学习的方法，通过使用数十万个未标记的web视频作为数据集，设计了一个具有排名损失函数的Siamese-Triplet网络，用于深度卷积神经网络的无监督学习，可在不使用ImageNet的情况下，获得52%的mAP的性能，并展示了此非监督网络在其它任务中表现出竞争性。

利用视频进行无监督学习的视觉表示