基于残差注意力的 LSTM 视频字幕识别.zip

# Residual Attention-based LSTM for Video Caption An implementation for paper "Residual Attention-based LSTM for Video Caption": https://link.springer.com/article/10.1007%2Fs11280-018-0531-z ## Requirements Python 2.7.6 Theano 0.8.2 ## processed data You need to download pretrained resnet model for extracting features. We provide our extracted ResNet video feature and processed caption in:https://drive.google.com/open?id=1HymvVvAEygM6UJm41dQkQ4IbTWcHT0iQ. Download this dataset and replace RAB_FEATURE_BASE_PATH in config.py with your feature path and replace RAB_DATASET_BASE_PATH in config.py with your processed data path. Besides, you should assign where to store your result in config.py. ## Evaluation If you'd like to evaluate BLEU/METEOR/CIDER scores during training. Don't forget to download coco-caption:https://github.com/tylin/coco-caption and Jobman:http://deeplearning.net/software/jobman/install.html. Also you should add coco-caption path to $PYTHONPATH and add jobman path to $PYTHONPATH as well. ## Others If you have any questions, drop us email at:xiangpengli.cs@gmail.com ``` @article{li2019residual, title={Residual attention-based LSTM for video captioning}, author={Li, Xiangpeng and Zhou, Zhilong and Chen, Lijiang and Gao, Lianli}, journal={World Wide Web}, volume={22}, number={2}, pages={621--636}, year={2019}, publisher={Springer} } ```