Caption Generation 比google的方法更快(6 hours v.s. several weeks)

https://github.com/kimiyoung/review_net

Review Network for Caption Generation

Image Captioning on MSCOCO

You can use the code in this repo to genearte a MSCOCO evaluation server submission with CIDEr=0.96+ with just a few hours.

No fine-tuning required. No fancy tricks. Just train three end-to-end review networks and do an ensemble.

• Feature extraction: 2 hours in parallel
• Single model training: 6 hours
• Ensemble model training: 30 mins
• Beam search for caption generation: 3 hours in parallel

Below is a comparison with other state-of-the-art systems (with according published papers) on the MSCOCO evaluation server:

In the diretcory image_caption_online, you can use the code therein to reproduce our evaluation server results.

In the directory image_caption_offline, you can rerun experiments in our paper using offline evaluation.

Code Captioning

Predicting comments for a piece of source code is another interesting task. In the repo we also release a dataset with train/dev/test splits, along with the code of a review network.

Check out the directory code_caption.

Below is a comparison with baselines on the code captioning dataset:

References

This repo contains the code and data used in the following paper:

Review Networks for Caption Generation

Zhilin Yang, Ye Yuan, Yuexin Wu, Ruslan Salakhutdinov, William W. Cohen

NIPS 2016