This repository includes the implementation for Attention on Attention for Image Captioning.

• Python 3.6
• Java 1.8.0
• PyTorch 1.0
• cider (already been added as a submodule)
• coco-caption (already been added as a submodule)
• tensorboardX

See details in data/README.md.

(notes: Set word_count_threshold in scripts/prepro_labels.py to 4 to generate a vocabulary of size 10,369.)

You should also preprocess the dataset and get the cache for calculating cider score for SCST:

$ python scripts/prepro_ngrams.py --input_json data/dataset_coco.json --dict_json data/cocotalk.json --output_pkl data/coco-train --split train

$ CUDA_VISIBLE_DEVICES=0 sh train.sh

See opts.py for the options. (You can download the pretrained models from here.)

$ CUDA_VISIBLE_DEVICES=0 python eval.py --model log/log_aoanet_rl/model.pth --infos_path log/log_aoanet_rl/infos_aoanet.pkl  --dump_images 0 --dump_json 1 --num_images -1 --language_eval 1 --beam_size 2 --batch_size 100 --split test

You will get the scores close to below after training under xe loss for 25 epochs:

{'Bleu_1': 0.7729384559899702, 'Bleu_2': 0.6163398035383025, 'Bleu_3': 0.4790123137715982, 'Bleu_4': 0.36944349063530374, 'METEOR': 0.2848188431924821, 'ROUGE_L': 0.5729849683867054, 'CIDEr': 1.1842173801790759, 'SPICE': 0.21650786258302354}

(notes: You can enlarge --max_epochs in train.sh to train the model for more epochs and improve the scores.)

after training under SCST loss for another 15 epochs, you will get:

{'Bleu_1': 0.8054903453672397, 'Bleu_2': 0.6523038976984842, 'Bleu_3': 0.5096621263772566, 'Bleu_4': 0.39140307771618477, 'METEOR': 0.29011216375635934, 'ROUGE_L': 0.5890369750273199, 'CIDEr': 1.2892294296245852, 'SPICE': 0.22680092759866174}

If you find this repo helpful, please consider citing:

@inproceedings{huang2019attention,
  title={Attention on Attention for Image Captioning},
  author={Huang, Lun and Wang, Wenmin and Chen, Jie and Wei, Xiao-Yong},
  booktitle={International Conference on Computer Vision},
  year={2019}
}

This repository is based on self-critical.pytorch, and you may refer to it for more details about the code.