https://github.com/kimiyoung/review_net
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.
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.
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:
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