Abstract
Neural encoder-decoder frameworks have been used extensively in image captioning. Recent research has shown that reinforcement learning can be utilized to train these frameworks directly on non-differentiable evaluation metrics. However, the captions generated by this method usually have limited grammaticality and readability. In this paper, we propose a novel model with the location-based mechanism which introduces the location information of each region in the image, and a combined training method that combines the cross entropy loss and reinforcement learning. We evaluate our model on four public benchmarks: Flickr8k, Flickr30k, MSCOCO and Image Chinese Captioning (ICC). Experimental results show that our model can improve the readability of the generated captions and outperforms the state-of-the-art methods across different evaluation metrics.
This work was supported by the National Natural Science Foundation of China (No. 61370137) and the Ministry of Education China Mobile Research Foundation Project (No. 2016/2-7).
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Zhao, L., Zhang, C., Zhang, X., Hu, Y., Niu, Z. (2018). A Deep Reinforced Training Method for Location-Based Image Captioning. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_67
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