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This paper strives to find the sentence best describing the content of animage or video. Different from existing works, which rely on a joint subspacefor image / video to sentence matching, we propose to do so in a visual spaceonly. We contribute Word2VisualVec, a deep neural network architecture thatlearns to predict a deep visual encoding of textual input based on sentencevectorization and a multi-layer perceptron. We thoroughly analyze itsarchitectural design, by varying the sentence vectorization strategy, networkdepth and the deep feature to predict for image to sentence matching. We alsogeneralize Word2VisualVec for matching a video to a sentence, by extending thepredictive abilities to 3-D ConvNet features as well as a visual-audiorepresentation. Experiments on four challenging image and video benchmarksdetail Word2VisualVec's properties, capabilities for image and video tosentence matching, and on all datasets its state-of-the-art results.

Word2VisualVec: Image and Video to Sentence Matching by Visual Feature Prediction