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• W2994783504 abstract "Adversarial learning has shown its advances in generating natural and diverse descriptions in image captioning. However, the learned reward of existing adversarial methods is vague and ill-defined due to the reward ambiguity problem. In this paper, we propose a refined Adversarial Inverse Reinforcement Learning (rAIRL) method to handle the reward ambiguity problem by disentangling reward for each word in a sentence, as well as achieve stable adversarial training by refining the loss function to shift the generator towards Nash equilibrium. In addition, we introduce a conditional term in the loss function to mitigate mode collapse and to increase the diversity of the generated descriptions. Our experiments on MS COCO and Flickr30K show that our method can learn compact reward for image captioning." @default.
• W2994783504 created "2019-12-26" @default.
• W2994783504 creator A5006748765 @default.
• W2994783504 creator A5063936933 @default.
• W2994783504 date "2020-03-24" @default.
• W2994783504 modified "2023-09-27" @default.
• W2994783504 title "Learning Compact Reward for Image Captioning" @default.
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