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We deal with the problem of semantic classification of challenging andhighly-cluttered dataset. We present a novel, and yet a very simpleclassification technique by leveraging the ease of classifiability of anyexisting well separable dataset for guidance. Since the guide dataset which mayor may not have any semantic relationship with the experimental dataset, formswell separable clusters in the feature set, the proposed network tries to embedclass-wise features of the challenging dataset to those distinct clusters ofthe guide set, making them more separable. Depending on the availability, wepropose two types of guide sets: one using texture (image) guides and anotherusing prototype vectors representing cluster centers. Experimental resultsobtained on the challenging benchmark RSSCN, LSUN, and TU-Berlin datasetsestablish the efficacy of the proposed method as we outperform the existingstate-of-the-art techniques by a considerable margin.

GuCNet: A Guided Clustering-based Network for Improved Classification