This repository contains the reference code for the paper Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions (CVPR 2019).
Please cite with the following BibTeX:
@inproceedings{cornia2019show,
title={{Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions}},
author={Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2019}
}
Clone the repository and create the sct conda environment using the conda.yml file:
conda env create -f conda.yml
conda activate sct
Our code is based on SpeakSee: a Python package that provides utilities for working with Visual-Semantic data, developed by us. The conda enviroment we provide already includes a beta version of this package.
Download the annotations and metadata file dataset_coco.tgz (~85.6 MB) and extract it in the code folder using tar -xzvf dataset_coco.tgz.
Download the pre-computed features file coco_detections.hdf5 (~53.5 GB) and place it under the datasets/coco folder, which gets created after decompressing the annotation file.
As before, download the annotations and metadata file dataset_flickr.tgz (~32.8 MB) and extract it in the code folder using tar -xzvf dataset_flickr.tgz.
Download the pre-computed features file flickr30k_detections.hdf5 (~13.1 GB) and place it under the datasets/flickr folder, which gets created after decompressing the annotation file.
To reproduce the results in the paper, download the pretrained model file saved_models.tgz (~4 GB) and extract it in the code folder with tar -xzvf saved_models.tgz.
Run python test_region_sequence.py using the following arguments:
| Argument | Possible values |
|---|---|
--dataset |
coco, flickr |
--exp_name |
ours, ours_without_visual_sentinel, ours_with_single_sentinel |
--sample_rl |
If used, tests the model with CIDEr optimization |
--sample_rl_nw |
If used, tests the model with CIDEr + NW optimization |
--batch_size |
Batch size (default: 16) |
--nb_workers |
Number of workers (default: 0) |
For example, to reproduce the results of our full model trained on COCO-Entities with CIDEr+NW optimization (Table 2, bottom right), use:
python test_region_sequence.py --dataset coco --exp_name ours --sample_rl_nw
Run python test_region_set.py using the following arguments:
| Argument | Possible values |
|---|---|
--dataset |
coco, flickr |
--exp_name |
ours, ours_without_visual_sentinel, ours_with_single_sentinel |
--sample_rl |
If used, tests the model with CIDEr optimization |
--sample_rl_nw |
If used, tests the model with CIDEr + NW optimization |
--batch_size |
Batch size (default: 16) |
--nb_workers |
Number of workers (default: 0) |
For example, to reproduce the results of our full model trained on COCO-Entities with CIDEr+NW optimization (Table 4, bottom row), use:
python test_region_set.py --dataset coco --exp_name ours --sample_rl_nw
Under logs/, you may also find the expected output of all experiments.
Run python train.py using the following arguments:
| Argument | Possible values |
|---|---|
--exp_name |
Experiment name |
--batch_size |
Batch size (default: 100) |
--lr |
Initial learning rate (default: 5e-4) |
--nb_workers |
Number of workers (default: 0) |
--sample_rl |
If used, the model will be trained with CIDEr optimization |
--sample_rl_nw |
If used, the model will be trained with CIDEr + NW optimization |
For example, to train the model with cross entropy, use:
python train.py --exp_name show_control_and_tell --batch_size 100 --lr 5e-4
To train the model with CIDEr optimization (after training the model with cross entropy), use:
python train.py --exp_name show_control_and_tell --batch_size 100 --lr 5e-5 --sample_rl
To train the model with CIDEr + NW optimization (after training the model with cross entropy), use:
python train.py --exp_name show_control_and_tell --batch_size 100 --lr 5e-5 --sample_rl_nw
Note: the current training code only supports the use of the COCO Entities dataset.
If you want to use only the annotations of our COCO Entities dataset, you can download the annotation file coco_entities_release.json (~403 MB).
The annotation file contains a python dictionary structured as follows:
coco_entities_release.json
└── <id_image>
└── <caption>
└── 'det_sequences'
└── 'noun_chunks'
└── 'detections'
└── 'split'
In details, for each image-caption pair, we provide the following information:
det_sequences, which contains a list of detection classes associated to each word of the caption (for an exact match with caption words, split the caption by spaces).Noneindicates the words that are not part of noun chunks, while_indicates noun chunk words for which an association with a detection in the image was not possible.noun_chunks, which is a list of tuples representing the noun chunks of the captions associated with a detection in the image. Each tuple is composed by two elements: the first one represents the noun chunk in the caption, while the second is the detection class associated to that noun chunk.detections, which contains a dictionary with a number of elements equal to the number of detection classes associated with at least a noun chunk in the caption. For each detection class, it provides a list of tuples representing the image regions detected by Faster R-CNN re-trained on Visual Genome [1] and corresponding to that detection class. Each tuple is composed by the detection id and the corresponding boundig box in the form[x1, y1, x2, y2]. The detection id can be used to recover the detection feature vector from the pre-computed features file coco_detections.hdf5 (~53.5 GB). See the demo section below for more details.split, which indicates the dataset split of that sample (i.e. train, val or test) following the COCO splits provided by [2].
Note that this annotation file includes all image-caption pairs for which at least one noun chunk-detection association has been found. However, in validation and testing phase of our controllable captioning model, we dropped all captions with empty region sets (i.e. those captions with at least one _ in the det_sequences field).
By downloading the dataset, you declare that you will use it for research and educational purposes only, any commercial use is prohibited.
An example of how to use the COCO Entities annotations can be found in the coco_entities_demo.ipynb file.
[1] P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang. Bottom-up and top-down attention for image captioning and visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018.
[2] A. Karpathy and L. Fei-Fei. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
If you have any general doubt about our work, please use the public issues section on this github repo. Alternatively, drop us an e-mail at marcella.cornia [at] unimore.it or lorenzo.baraldi [at] unimore.it.
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