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arXiv · 1803.11209

Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts

Raymond A. Yeh·Jinjun Xiong·Wen-mei W. Hwu·Minh N. Do·Alexander G. Schwing
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Abstract

Textual grounding is an important but challenging task for human-computer interaction, robotics and knowledge mining. Existing algorithms generally formulate the task as selection from a set of bounding box proposals obtained from deep net based systems. In this work, we demonstrate that we can cast the problem of textual grounding into a unified framework that permits efficient search over all possible bounding boxes. Hence, the method is able to consider significantly more proposals and doesn't rely on a successful first stage hypothesizing bounding box proposals. Beyond, we demonstrate that the trained parameters of our model can be used as word-embeddings which capture spatial-image relationships and provide interpretability. Lastly, at the time of submission, our approach outperformed the current state-of-the-art methods on the Flickr 30k Entities and the ReferItGame dataset by 3.08% and 7.77% respectively.

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Raymond A. Yeh, Jinjun Xiong, Wen-mei W. Hwu, Minh N. Do, Alexander G. Schwing. 2018-03-29. Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts. https://arxiv.org/abs/1803.11209

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