This work is a kernel formulation that predicts visual classifiers in the form given by the representer theorem. We also propose a distributional semantic kernel between text descriptions which is shown to be useful in our setting.  1- Mohamed Elhoseiny, Ahmed Elgammal, Babak Saleh, "Tell and Predict: Kernel Classifier Prediction for Unseen Visual Classes from Unstructured Text Descriptions“, Arxiv, 2015. Presentations  1- Mohamed Elhoseiny, Ahmed Elgammal, Babak Saleh, "Tell and Predict: Kernel Classifier Prediction for Unseen Visual Classes from Unstructured Text Descriptions“, CVPR Workshop on Language and Vision, 2015. link  2- Mohamed Elhoseiny, Ahmed Elgammal, Babak Saleh, "Visual Classifier Prediction by Distributional Semantic Embedding of Text Descriptions“, EMNLP Workshop on Vision and Language, 2015. link Code Word2vec Model  link
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