  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.
