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Photos are becoming spontaneous, objective, and universal sources ofinformation. This paper develops evolving situation recognition using photostreams coming from disparate sources combined with the advances of deeplearning. Using visual concepts in photos together with space and timeinformation, we formulate the situation detection into a semi-supervisedlearning framework and propose new graph-based models to solve the problem. Toextend the method for unknown situations, we introduce a soft label methodwhich enables the traditional semi-supervised learning framework to accuratelypredict predefined labels as well as effectively form new clusters. To overcomethe noisy data which degrades graph quality, leading to poor recognitionresults, we take advantage of two kinds of noise-robust norms which caneliminate the adverse effects of outliers in visual concepts and improve theaccuracy of situation recognition. Finally, we demonstrate the idea and theeffectiveness of the proposed model on Yahoo Flickr Creative Commons 100Million.

From Photo Streams to Evolving Situations