Details

Autor(en) / Beteiligte

• Licheng Yu
• Hao Tan
• Bansal, Mohit
• Berg, Tamara L.

Titel

A Joint Speaker-Listener-Reinforcer Model for Referring Expressions

Ist Teil von

• 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, p.3521-3529

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Referring expressions are natural language constructions 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 reinforcer introduces a reward function to guide sampling of more discriminative expressions. The listener-speaker modules are trained jointly in an end-to-end learning framework, allowing the modules to be aware of one another during learning while also benefiting from the discriminative 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 expression datasets.