| #update: 17-6-2023 add both soft/hard-label to visual_caption_cosine_score | |
| # Introduction | |
| Modern image captaining relies heavily on extracting knowledge, from images such as objects, | |
| to capture the concept of static story in the image. In this paper, we propose a textual visual context dataset | |
| for captioning, where the publicly available dataset COCO caption (Lin et al., 2014) has been extended with information | |
| about the scene (such as objects in the image). Since this information has textual form, it can be used to leverage any NLP task, | |
| such as text similarity or semantic relation methods, into captioning systems, either as an end-to-end training strategy or a post-processing based approach. | |
| Please refer to [project page](https://sabirdvd.github.io/project_page/Dataset_2022/index.html) and [Github](https://github.com/ahmedssabir/Visual-Semantic-Relatedness-Dataset-for-Image-Captioning) for more information. [](https://arxiv.org/abs/2301.08784) [](https://ahmed.jp/project_page/Dataset_2022/index.html) | |
| For quick start please have a look this [demo](https://github.com/ahmedssabir/Textual-Visual-Semantic-Dataset/blob/main/BERT_CNN_Visual_re_ranker_demo.ipynb) and [pre-trained model with th 0.2, 0.3, 0.4](https://huggingface.co/AhmedSSabir/BERT-CNN-Visual-Semantic) | |
| # Overview | |
| We enrich COCO-Caption with textual Visual Context information. We use ResNet152, CLIP, | |
| and Faster R-CNN to extract object information for each image. We use three filter approaches | |
| to ensure the quality of the dataset (1) Threshold: to filter out predictions where the object classifier | |
| is not confident enough, and (2) semantic alignment with semantic similarity to remove duplicated objects. | |
| (3) semantic relatedness score as soft-label: to guarantee the visual context and caption have a strong | |
| relation. In particular, we use Sentence-RoBERTa via cosine similarity to give a soft score, and then | |
| we use a threshold to annotate the final label (if th ≥ 0.2, 0.3, 0.4 then 1,0). Finally, to take advantage | |
| of the visual overlap between caption and visual context, and to extract global information, we use BERT followed by a shallow CNN (<a href="https://arxiv.org/abs/1408.5882">Kim, 2014</a>) | |
| to estimate the visual relatedness score. | |
| <!-- | |
| ## Dataset | |
| ### Sample | |
| ``` | |
| |---------------+--------------+---------+---------------------------------------------------| | |
| | VC1 | VC2 | VC3 | human annoated caption | | |
| | ------------- | ----------- | --------| ------------------------------------------------- | | |
| | cheeseburger | plate | hotdog | a plate with a hamburger fries and tomatoes | | |
| | bakery | dining table | website | a table having tea and a cake on it | | |
| | gown | groom | apron | its time to cut the cake at this couples wedding | | |
| |---------------+--------------+---------+---------------------------------------------------| | |
| ``` | |
| --> | |
| ### Download | |
| 0. [Dowload Raw data with ID and Visual context](https://www.dropbox.com/s/xuov24on8477zg8/All_Caption_ID.csv?dl=0) -> original dataset with related ID caption [train2014](https://cocodataset.org/#download) | |
| 1. [Downlod Data with cosine score](https://www.dropbox.com/s/u1n2r2ign8v7gvh/visual_caption_cosine_score.zip?dl=0)-> soft cosine lable with **th** 0.2, 0.3, 0.4 and 0.5 | |
| 2. [Dowload Overlaping visual with caption](https://www.dropbox.com/s/br8nhnlf4k2czo8/COCO_overlaping_dataset.txt?dl=0)-> Overlap visual context and the human annotated caption | |
| 3. [Download Dataset (tsv file)](https://www.dropbox.com/s/dh38xibtjpohbeg/train_all.zip?dl=0) 0.0-> raw data with hard lable without cosine similairty and with **th**reshold cosine sim degree of the relation beteween the visual and caption = 0.2, 0.3, 0.4 | |
| 4. [Download Dataset GenderBias](https://www.dropbox.com/s/1wki0b0d21078mj/gender%20natural.zip?dl=0)-> man/woman replaced with person class label | |
| For unsupervised learning | |
| 1. [Download CC](https://www.dropbox.com/s/pc1uv2rf6nqdp57/CC_caption_40.txt.zip) -> Caption dataset from Conceptinal Caption (CC) 2M (2255927 captions) | |
| 2. [Download CC+wiki](https://www.dropbox.com/s/xuov24on8477zg8/All_Caption_ID.csv?dl=0) -> CC+1M-wiki 3M (3255928) | |
| 3. [Download CC+wiki+COCO](https://www.dropbox.com/s/k7oqwr9a1a0h8x1/CC_caption_40%2Bwiki%2BCOCO.txt.zip) -> CC+wiki+COCO-Caption 3.5M (366984) | |
| 4. [Download COCO-caption+wiki](https://www.dropbox.com/s/wc4k677wp24kzhh/COCO%2Bwiki.txt.zip) -> COCO-caption +wiki 1.4M (1413915) | |
| 5. [Download COCO-caption+wiki+CC+8Mwiki](https://www.dropbox.com/s/xhfx32sjy2z5bpa/11M_wiki_7M%2BCC%2BCOCO.txt.zip) -> COCO-caption+wiki+CC+8Mwiki 11M (11541667) | |
| ## Citation | |
| The details of this repo are described in the following paper. If you find this repo useful, please kindly cite it: | |
| ```bibtex | |
| @article{sabir2023visual, | |
| title={Visual Semantic Relatedness Dataset for Image Captioning}, | |
| author={Sabir, Ahmed and Moreno-Noguer, Francesc and Padr{\'o}, Llu{\'\i}s}, | |
| journal={arXiv preprint arXiv:2301.08784}, | |
| year={2023} | |
| } | |
| ``` | |