Papers › Recurrent Models for Situation Recognition

Recurrent Models for Situation Recognition

18 Mar 2017ICCV 2017 10arXiv:1703.06233archive 2025-07-28

Arun Mallya, Svetlana Lazebnik

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.

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