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The current state-of-the-art in feature learning relies on the supervisedlearning of large-scale datasets consisting of target content items and theirrespective category labels. However, constructing such large-scalefully-labeled datasets generally requires painstaking manual effort. Onepossible solution to this problem is to employ community contributed text tagsas weak labels, however, the concepts underlying a single text tag stronglydepends on the users. We instead present a new paradigm for learningdiscriminative features by making full use of the human curation process onsocial networking services (SNSs). During the process of content curation, SNSusers collect content items manually from various sources and group them bycontext, all for their own benefit. Due to the nature of this process, we canassume that (1) content items in the same group share the same semantic conceptand (2) groups sharing the same images might have related semantic concepts.Through these insights, we can define human curated groups as weak labels fromwhich our proposed framework can learn discriminative features as arepresentation in the space of semantic concepts the users intended whencreating the groups. We show that this feature learning can be formulated as aproblem of link prediction for a bipartite graph whose nodes corresponds tocontent items and human curated groups, and propose a novel method for featurelearning based on sparse coding or network fine-tuning.

Weakly supervised collective feature learning from curated media