Deep Reinforcement Learning-based Image Captioning with Embedding Reward
2017-10-09 11:58
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Deep Reinforcement Learning-based Image Captioning with Embedding Reward
Zhou Ren, Xiaoyu Wang, NingZhang, Xutao Lv, Li-Jia Li
(Submitted on 12 Apr 2017)
Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep neural networks have substantially improved the performance of this task. Most
state-of-the-art approaches follow an encoder-decoder framework, which generates captions using a sequential recurrent prediction model. However, in this paper, we introduce a novel decision-making framework for image captioning. We utilize a "policy network"
and a "value network" to collaboratively generate captions. The policy network serves as a local guidance by providing the confidence of predicting the next word according to the current state. Additionally, the value network serves as a global and lookahead
guidance by evaluating all possible extensions of the current state. In essence, it adjusts the goal of predicting the correct words towards the goal of generating captions similar to the ground truth captions. We train both networks using an actor-critic
reinforcement learning model, with a novel reward defined by visual-semantic embedding. Extensive experiments and analyses on the Microsoft COCO dataset show that the proposed framework outperforms state-of-the-art approaches across different evaluation metrics.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:1704.03899 [cs.CV] |
| (or arXiv:1704.03899v1 [cs.CV] for this version) |
Submission history
From: Xiaoyu Wang [view email][v1] Wed, 12 Apr 2017 18:55:03 GMT (5922kb,D)
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