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Guessing objects in context

Large scale object classification has seen commendable progress owing, in large part, to recent advances in deep learning. However, generating annotated training datasets is still a significant challenge, especially when training classifiers for large number of object categories. In these situations, generating training datasets is expensive coupled with the fact that training data may not be available for all categories and situations. Such situations are generally resolved using zero-shot learning. However, training zero-shot classifiers entails serious programming effort and is not scalable to very large number of object categories. We propose a novel simple framework that can guess objects in an image. The proposed framework has the advantages of scalability and ease of use with minimal loss in accuracy. The proposed framework answers the following question: How does one guess objects in an image from very few object detections?