Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning

Author:

Honda Ukyo1,Watanabe Taro2,Matsumoto Yuji3

Affiliation:

1. CyberAgent, Inc

2. Nara Institute of Science and Technology

3. RIKEN

Publisher

IEEE

Reference79 articles.

1. Attention is all you need;vaswani;NeurIPS,2017

2. Stolen Probability: A Structural Weakness of Neural Language Models

3. Long-tailed classification by keeping the good and removing the bad momentum causal effect;tang;NeurIPS,2020

4. Contrastive learning for image captioning;dai;NeurIPS,2017

5. CIDEr: Consensus-based image description evaluation

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2. Automatic Captioning based on Visible and Infrared Images;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. GAGPT-2: A Geometric Attention-based GPT-2 Framework for Image Captioning in Hindi;ACM Transactions on Asian and Low-Resource Language Information Processing;2023-10-13

4. Image Captioning: A Comprehensive Survey, Comparative Analysis of Existing Models, and Research Gaps;2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS);2023-08-23

5. Deep image captioning: A review of methods, trends and future challenges;Neurocomputing;2023-08

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