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--- |
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task_categories: |
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- image-to-text |
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- image-classification |
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- visual-question-answering |
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- sentence-similarity |
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language: |
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- en |
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tags: |
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- image captioning |
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- language grounding |
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- visual semantic |
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- semantic similarity |
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pretty_name: ' image captioning language grounding visual semantic ' |
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--- |
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#### Update: OCT-2023 ### |
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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) |
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# Introduction |
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Modern image captaining relies heavily on extracting knowledge, from images such as objects, |
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to capture the concept of static story in the image. In this paper, we propose a textual visual context dataset |
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for captioning, where the publicly available dataset COCO caption (Lin et al., 2014) has been extended with information |
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about the scene (such as objects in the image). Since this information has textual form, it can be used to leverage any NLP task, |
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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. |
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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) |
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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) |
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# Overview |
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We enrich COCO-Caption with textual Visual Context information. We use ResNet152, CLIP, |
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and Faster R-CNN to extract object information for each image. We use three filter approaches |
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to ensure the quality of the dataset (1) Threshold: to filter out predictions where the object classifier |
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is not confident enough, and (2) semantic alignment with semantic similarity to remove duplicated objects. |
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(3) semantic relatedness score as soft-label: to guarantee the visual context and caption have a strong |
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relation. In particular, we use Sentence-RoBERTa-sts via cosine similarity to give a soft score, and then |
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we use a threshold to annotate the final label (if th ≥ 0.2, 0.3, 0.4 then 1,0). Finally, to take advantage |
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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) |
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to estimate the visual relatedness score. |
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<!-- |
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## Dataset |
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(<a href="https://arxiv.org/abs/1408.5882">Kim, 2014</a>) |
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### Sample |
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``` |
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|---------------+--------------+---------+---------------------------------------------------| |
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| VC1 | VC2 | VC3 | human annoated caption | |
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| ------------- | ----------- | --------| ------------------------------------------------- | |
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| cheeseburger | plate | hotdog | a plate with a hamburger fries and tomatoes | |
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| bakery | dining table | website | a table having tea and a cake on it | |
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| gown | groom | apron | its time to cut the cake at this couples wedding | |
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|---------------+--------------+---------+---------------------------------------------------| |
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``` |
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--> |
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### Download |
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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) |
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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] |
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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 |
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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 |
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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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<!-- |
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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 |
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employing unsupervised learning (i.e. contrastive learning). (work in progress) |
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# |
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1. [Download CC](https://www.dropbox.com/s/pc1uv2rf6nqdp57/CC_caption_40.txt.zip) -> Caption dataset from Conceptinal Caption (CC) 2M (2255927 captions) |
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2. [Download CC+wiki](https://www.dropbox.com/s/xuov24on8477zg8/All_Caption_ID.csv?dl=0) -> CC+1M-wiki 3M (3255928) |
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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) |
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4. [Download COCO-caption+wiki](https://www.dropbox.com/s/wc4k677wp24kzhh/COCO%2Bwiki.txt.zip) -> COCO-caption +wiki 1.4M (1413915) |
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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) |
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---> |
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## Citation |
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The details of this repo are described in the following paper. If you find this repo useful, please kindly cite it: |
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```bibtex |
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@article{sabir2023visual, |
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title={Visual Semantic Relatedness Dataset for Image Captioning}, |
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author={Sabir, Ahmed and Moreno-Noguer, Francesc and Padr{\'o}, Llu{\'\i}s}, |
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journal={arXiv preprint arXiv:2301.08784}, |
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year={2023} |
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} |
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``` |
