@inproceedings{shi2020improving,
  title={Improving Image Captioning with Better Use of Caption},
  author={Shi, Zhan and Zhou, Xu and Qiu, Xipeng and Zhu, Xiaodan},
  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  pages={7454--7464},
  year={2020}
}

python 2.7.15

torch 1.0.1

Specific conda env is shown in ezs.yml

BTW, you need to download coco-captions and cider folder in this directory for evaluation.

Files: Add files in data directory in google drive or [baidu netdisk](链接：https://pan.baidu.com/s/1ddtfdlwD65cm4JmVu6GF3w 提取码：39pa) to data directory here. See data/README for more details.

Models: Add log directory in google drive or or [baidu netdisk](链接：https://pan.baidu.com/s/1ddtfdlwD65cm4JmVu6GF3w 提取码：39pa) here.

MLE training:

python train.py --gpus 0 --id experiment-mle

RL training

python train.py --gpus 0 --id experiment-rl --learning_rate 2e-5 --resume_from experiment-mle --resume_from_best True --self_critical_after 0 --max_epochs 60 --learning_rate_decay_start -1 --scheduled_sampling_start -1 --reduce_on_plateau

Evaluate your own model or Load trained model:

python eval.py --gpus 0 --resume_from experiment-mle

and

python eval.py --gpus 0 --resume_from experiment-rl

This code is based on Ruotian Luo's brilliant image captioning repo ruotianluo/self-critical.pytorch. We use the detected bounding boxes/categories/features provided by Bottom-Up peteanderson80/bottom-up-attention, yangxuntu/SGAE. Many thanks for their work!