Knowledge Commons of Institute of Automation,CAS
Image Captioning with Word Gate and Adaptive Self-Critical Learning | |
Zhu, Xinxin1,2; Li, Lixiang1,2; Liu, Jing3; Guo, Longteng3; Fang, Zhiwei3; Peng, Haipeng1,2; Niu, Xinxin1,2 | |
发表期刊 | APPLIED SCIENCES-BASEL |
ISSN | 2076-3417 |
2018-06-01 | |
卷号 | 8期号:6页码:13 |
摘要 | Although the policy-gradient methods for reinforcement learning have shown significant improvement in image captioning, how to achieve high performance during the reinforcement optimizing process is still not a simple task. There are at least two difficulties: (1) The large size of vocabulary leads to a large action space, which makes it difficult for the model to accurately predict the current word. (2) The large variance of gradient estimation in reinforcement learning usually causes severe instabilities in the training process. In this paper, we propose two innovations to boost the performance of self-critical sequence training (SCST). First, we modify the standard long short-term memory (LSTM)based decoder by introducing a gate function to reduce the search scope of the vocabulary for any given image, which is termed the word gate decoder. Second, instead of only considering current maximum actions greedily, we propose a stabilized gradient estimation method whose gradient variance is controlled by the difference between the sampling reward from the current model and the expectation of the historical reward. We conducted extensive experiments, and results showed that our method could accelerate the training process and increase the prediction accuracy. Our method was validated on MS COCO datasets and yielded state-of-the-art performance. |
关键词 | image caption image understanding deep learning computer vision |
DOI | 10.3390/app8060909 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2016YFB0800602] ; National Natural Science Foundation of China[61771071] ; National Natural Science Foundation of China[61573067] ; National Natural Science Foundation of China[61573067] ; National Natural Science Foundation of China[61771071] ; National Key Research and Development Program of China[2016YFB0800602] |
WOS研究方向 | Chemistry ; Materials Science ; Physics |
WOS类目 | Chemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied |
WOS记录号 | WOS:000436488000068 |
出版者 | MDPI |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26325 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
通讯作者 | Li, Lixiang |
作者单位 | 1.Beijing Univ Posts & Telecommun, Informat Secur Ctr, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China 2.Beijing Univ Posts & Telecommun, Natl Engn Lab Disaster Backup & Recovery, Beijing 100876, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Zhu, Xinxin,Li, Lixiang,Liu, Jing,et al. Image Captioning with Word Gate and Adaptive Self-Critical Learning[J]. APPLIED SCIENCES-BASEL,2018,8(6):13. |
APA | Zhu, Xinxin.,Li, Lixiang.,Liu, Jing.,Guo, Longteng.,Fang, Zhiwei.,...&Niu, Xinxin.(2018).Image Captioning with Word Gate and Adaptive Self-Critical Learning.APPLIED SCIENCES-BASEL,8(6),13. |
MLA | Zhu, Xinxin,et al."Image Captioning with Word Gate and Adaptive Self-Critical Learning".APPLIED SCIENCES-BASEL 8.6(2018):13. |
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