Code for "simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions" (EMNLP 2018)
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simNet

Implementation of "simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions" by Fenglin Liu, Xuancheng Ren, Yuanxin Liu, Houfeng Wang, and Xu Sun. The paper can be found at [arxiv].

Usage

Requirements

This code is written in Python2.7 and requires PyTorch 0.3

You need to download pre-trained Resnet152 model from torchvision for both training and evaluation.

You may take a look at https://github.com/s-gupta/visual-concepts to find how to get the topic words of an image.

Training a simNet model

Now we can train our simNet model with

CUDA_VISIBLE_DEVICES=1,2,3 screen python train.py

Testing a trained model

We can test our simNet model with

CUDA_VISIBLE_DEVICES=1,2,3 screen python test.py

Reference

If you use this code as part of any published research, please acknowledge the following paper

@inproceedings{Liu2018simNet,
author = {Fenglin Liu and Xuancheng Ren and Yuanxin Liu and Houfeng Wang and Xu Sun},
title = {sim{N}et: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions},
booktitle = {EMNLP 2018},
year = {2018}
}

Acknowledgements

Thanks to Torch team for providing Torch 0.3, CodaLab team for providing online evaluation, COCO team and Flickr30k for providing dataset, Tsung-Yi Lin for providing evaluation codes for MS COCO caption generation, Yufeng Ma's open source repositories and Torchvision ResNet implementation.

Note

If you have any questions about the code or our paper, please send an email to lfl@bupt.edu.cn