| | --- |
| | task_categories: |
| | - image-to-text |
| | language: |
| | - en |
| | tags: |
| | - image captioning |
| | pretty_name: ' image captioning ' |
| | size_categories: |
| | - n<1K |
| | --- |
| | #### Update: OCT-2023 ### |
| | Add v2 with recent SoTA model **swinV2 classifier** for both soft/*hard-label* visual_caption_cosine_score_v2 with _person_ label (0.2, 0.3 and 0.4) |
| |
|
| |
|
| | # 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-sts 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 1D-CNN (Kim, 2014) |
| | to estimate the visual relatedness score. |
| | |
| | |
| | |
| | <!-- |
| | ## Dataset |
| | (<a href="https://arxiv.org/abs/1408.5882">Kim, 2014</a>) |
| | ### 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/55sit8ow9tems4u/visual_caption_cosine_score.zip?dl=0)-> soft cosine lable with **th** 0.2, 0.3, 0.4 and 0.5 and hardlabel [0,1] |
| | 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 |
| |
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|
| | For future work, we plan to extract the visual context from the caption (without using a visual classifier) and estimate the visual relatedness score by |
| | employing unsupervised learning (i.e. contrastive learning). (work in progress) |
| | # |
| | 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} |
| | } |
| | ``` |
