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arXiv · 1904.03885

Referring to Objects in Videos using Spatio-Temporal Identifying Descriptions

Peratham Wiriyathammabhum·Abhinav Shrivastava·Vlad I. Morariu·Larry S. Davis
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Abstract

This paper presents a new task, the grounding of spatio-temporal identifying descriptions in videos. Previous work suggests potential bias in existing datasets and emphasizes the need for a new data creation schema to better model linguistic structure. We introduce a new data collection scheme based on grammatical constraints for surface realization to enable us to investigate the problem of grounding spatio-temporal identifying descriptions in videos. We then propose a two-stream modular attention network that learns and grounds spatio-temporal identifying descriptions based on appearance and motion. We show that motion modules help to ground motion-related words and also help to learn in appearance modules because modular neural networks resolve task interference between modules. Finally, we propose a future challenge and a need for a robust system arising from replacing ground truth visual annotations with automatic video object detector and temporal event localization.

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Peratham Wiriyathammabhum, Abhinav Shrivastava, Vlad I. Morariu, Larry S. Davis. 2019-04-08. Referring to Objects in Videos using Spatio-Temporal Identifying Descriptions. https://arxiv.org/abs/1904.03885

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