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• W2950791135 abstract "This work proposes Recurrent Neural Network (RNN) models to predict structured 'image situations' -- actions and noun entities fulfilling semantic roles related to the action. In contrast to prior work relying on Conditional Random Fields (CRFs), we use a specialized action prediction network followed by an RNN for noun prediction. Our system obtains state-of-the-art accuracy on the challenging recent imSitu dataset, beating CRF-based models, including ones trained with additional data. Further, we show that specialized features learned from situation prediction can be transferred to the task of image captioning to more accurately describe human-object interactions." @default.
• W2950791135 created "2019-06-27" @default.
• W2950791135 creator A5005340983 @default.
• W2950791135 creator A5017650914 @default.
• W2950791135 date "2017-03-18" @default.
• W2950791135 modified "2023-09-27" @default.
• W2950791135 title "Recurrent Models for Situation Recognition" @default.
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• W2950791135 hasPublicationYear "2017" @default.
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