Supervised (pre-)training currently yields state-of-the-art performance for
representation learning for visual recognition, yet it comes at the cost of (1)
intensive manual annotations and (2) an inherent restriction in the scope of
data relevant for learning. In this work, we explore unsupervised feature
learning from unlabeled video. We introduce a novel object-centric approach to
temporal coherence that encourages similar representations to be learned for
object-like regions segmented from nearby frames. Our framework relies on a
Siamese-triplet network to train a deep convolutional neural network (CNN)
representation. Compared to existing temporal coherence methods, our idea has
the advantage of lightweight preprocessing of the unlabeled video (no tracking
required) while still being able to extract object-level regions from which to
learn invariances. Furthermore, as we show in results on several standard
datasets, our method typically achieves substantial accuracy gains over
competing unsupervised methods for image classification and retrieval tasks.

本研究介绍了一种从未标记的视频中进行无监督学习的新方法，通过引入一种面向物体的时间相干性方法来促进学习具有相似表征的物体，并在多个基准数据集上展示了与竞争无监督方法相比显著的准确度提高。

无标签视频中的物体中心表示学习

Object-Centric Representation Learning from Unlabeled Videos

Is strong supervision necessary for learning a good visual representation? Do
we really need millions of semantically-labeled images to train a Convolutional
Neural Network (CNN)? In this paper, we present a simple yet surprisingly
powerful approach for unsupervised learning of CNN. Specifically, we use
hundreds of thousands of unlabeled videos from the web to learn visual
representations. Our key idea is that visual tracking provides the supervision.
That is, two patches connected by a track should have similar visual
representation in deep feature space since they probably belong to the same
object or object part. We design a Siamese-triplet network with a ranking loss
function to train this CNN 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 competitively 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。