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Title: A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
Referring expressions are natural language construc- tions used to identify particular objects within a scene. In this paper, we propose a unified framework for the tasks of referring expression comprehension and generation. Our model is composed of three modules: speaker, listener, and reinforcer. The speaker generates referring expressions, the listener comprehends referring expressions, and the rein- forcer introduces a reward function to guide sampling of more discriminative expressions. The listener-speaker mod- ules are trained jointly in an end-to-end learning frame- work, allowing the modules to be aware of one another during learning while also benefiting from the discrimina- tive reinforcer’s feedback. We demonstrate that this unified framework and training achieves state-of-the-art results for both comprehension and generation on three referring ex- pression datasets.  more » « less
Award ID(s):
1633295
NSF-PAR ID:
10038499
Author(s) / Creator(s):
Date Published:
Journal Name:
IEEE Conference on Computer Vision and Pattern Recognition
Volume:
1
Issue:
1
Page Range / eLocation ID:
1
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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