Awesome

Dense Relational Image Captioning

The code for our CVPR 2019 paper along with our journal extention paper (arXiv),

Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning.

Done by Dong-Jin Kim, Jinsoo Choi, Tae-Hyun Oh, and In So Kweon.

Link: arXiv , arXiv (Journal Extension) , Dataset, Pre-trained model.

Updates

(28/08/2019)

• Our code is updated from evaluation-only to trainable version.
• Codes for backpropagation part are added to several functions.

(06/09/2019)

• Fixed the bug of UnionSlicer code.
• Added eval_utils_mAP.lua.

(24/12/2020)

• Added the code for running our model on new images.

(26/01/2021)

• Updated the model in the journal extension version (MTTSNet+REM).

Installation

Some of the codes are built upon DenseCap: Fully Convolutional Localization Networks for Dense Captioning [website]. We appreciate them for their great work.

Our code is implemented in Torch, and depends on the following packages: torch/torch7, torch/nn, torch/nngraph, torch/image, lua-cjson, qassemoquab/stnbhwd, jcjohnson/torch-rnn. You'll also need to install torch/cutorch and torch/cunn;

After installing torch, you can install / update these dependencies by running the following:

luarocks install torch
luarocks install nn
luarocks install image
luarocks install lua-cjson
luarocks install https://raw.githubusercontent.com/qassemoquab/stnbhwd/master/stnbhwd-scm-1.rockspec
luarocks install https://raw.githubusercontent.com/jcjohnson/torch-rnn/master/torch-rnn-scm-1.rockspec
luarocks install cutorch
luarocks install cunn
luarocks install cudnn

Pre-trained model

You can download a pretrained Relational Captioning model from this link: Pre-trained models for either CVPR19 version or Journal version.

Download the model and place it in ./.

This is not the exact model that was used in the paper, but with different hyperparameters. it achieve a recall of 36.2 on the test set which is better than the reall of 34.27 that we report in the paper.

Running on new images

To run the model on new images, use the script run_model.lua. To run the pretrained model on an image, use the following command:

th run_model.lua -input_image /path/to/my/image/file.jpg

By default this will run in GPU mode; to run in CPU only mode, simply add the flag -gpu -1.

If you have an entire directory of images on which you want to run the model, use the -input_dir flag instead:

th run_model.lua -input_dir /path/to/my/image/folder

This run the model on all files in the folder /path/to/my/image/folder/ whose filename does not start with ..

Evaluation

To evaluate a model on our Relational Captioning Dataset, please follow the following steps:

1. Download the raw images from Visual Genome dataset version 1.2 website. Place the images in ./data/visual-genome/VG_100K.
2. Download our relational captioning label from the following link: Dataset. Place the json file at ./data/visual-genome/1.2/.
3. Use the script preprocess.py to generate a single HDF5 file containing the entire dataset.
4. Run script/setup_eval.sh to download and unpack METEOR jarfile.
5. Use the script evaluate_model.lua to evaluate a trained model on the validation or test data either with CVPR 2019 version (MTTSNet):

th evaluate_model.lua -checkpoint checkpoint_VGlongv3_tLSTM_MTL2_1e6.t7

or with recent journal extension version (MTTSNet+REM):

th evaluate_model.lua -checkpoint checkpoint_VGlongv3_REM_tLSTM_MTL2_512_FC+nonlinear_1e6.t7

6. If you want to measure the mAP metric, change the line9 from imRecall to mAP and run evaluate_model.lua.

Training

To train a model on our Relational Captioning Dataset, you can simply follow these steps:

1. Run script/download_models.sh to download VGG16 model.
2. Run train.lua to train a relational captioner. As default, the option -REM is set to be 1 which is for the journal version model (MTTSNet+REM). If you want to train a CVPR19 version model (MTTSNet), set the option -REM to be 0:

th train.lua -REM 0

The Recall and METEOR scores for the provided model for are as follows:

References:

[1] Johnson, J., Karpathy, A., & Fei-Fei, L. (2016). Densecap: Fully convolutional localization networks for dense captioning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4565-4574).

[2] Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (2018). Neural motifs: Scene graph parsing with global context. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 5831-5840).

Citation

If you find our work useful in your research, please consider citing our CVPR2019 paper or our TPAMI version paper:

@inproceedings{kim2019dense,
  title={Dense relational captioning: Triple-stream networks for relationship-based captioning},
  author={Kim, Dong-Jin and Choi, Jinsoo and Oh, Tae-Hyun and Kweon, In So},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={6271--6280},
  year={2019}
}

@article{kim2021dense,
  title={Dense relational image captioning via multi-task triple-stream networks},
  author={Kim, Dong-Jin and Oh, Tae-Hyun and Choi, Jinsoo and Kweon, In So},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  publisher={IEEE}
}