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Integrating higher level visual and linguistic interpretations is at theheart of human intelligence. As automatic visual category recognition in imagesis approaching human performance, the high level understanding in the dynamicspatiotemporal domain of videos and its translation into natural language isstill far from being solved. While most works on vision-to-text translationsuse pre-learned or pre-established computational linguistic models, in thispaper we present an approach that uses vision alone to efficiently learn how totranslate into language the video content. We discover, in simple form, thestory played by main actors, while using only visual cues for representingobjects and their interactions. Our method learns in a hierarchical mannerhigher level representations for recognizing subjects, actions and objectsinvolved, their relevant contextual background and their interaction to oneanother over time. We have a three stage approach: first we take inconsideration features of the individual entities at the local level ofappearance, then we consider the relationship between these objects and actionsand their video background, and third, we consider their spatiotemporalrelations as inputs to classifiers at the highest level of interpretation.Thus, our approach finds a coherent linguistic description of videos in theform of a subject, verb and object based on their role played in the overallvisual story learned directly from training data, without using a knownlanguage model. We test the efficiency of our approach on a large scale datasetcontaining YouTube clips taken in the wild and demonstrate state-of-the-artperformance, often superior to current approaches that use more complex,pre-learned linguistic knowledge.

Stories in the Eye: Contextual Visual Interactions for Efficient Video to Language Translation