usage: train_v7w_embedding.py [-h] [--gpu_id GPU_ID] [--batch_size BATCH_SIZE]
                              [--max_negative_answer MAX_NEGATIVE_ANSWER]
                              [--answer_batch_size ANSWER_BATCH_SIZE]
                              [--loss_temperature LOSS_TEMPERATURE]
                              [--pretrained_model PRETRAINED_MODEL]
                              [--context_embedding {SAN,BoW}]
                              [--answer_embedding {BoW,RNN}] [--name NAME]
optional arguments:
  -h, --help            show this help message and exit
  --gpu_id GPU_ID
  --batch_size BATCH_SIZE
  --max_negative_answer MAX_NEGATIVE_ANSWER
  --answer_batch_size ANSWER_BATCH_SIZE
  --loss_temperature LOSS_TEMPERATURE
  --pretrained_model PRETRAINED_MODEL
  --context_embedding {SAN,BoW}
  --answer_embedding {BoW,RNN}
  --name NAME

@inproceedings{hu2018learning,
  title={Learning Answer Embeddings for Visual Question Answering},
  author={Hu, Hexiang and Chao, Wei-Lun and Sha, Fei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5428--5436},
  year={2018}
}