--- pretty_name: InpaintCOCO language: - en size_categories: - 1K Many multimodal tasks, such as Vision-Language Retrieval and Visual Question Answering, present results in terms of overall performance. > Unfortunately, this approach overlooks more nuanced concepts, leaving us unaware of which specific concepts contribute to the success of current models and which are ignored. > In response to this limitation, more recent benchmarks attempt to assess particular aspects of vision-language models. > Some existing datasets focus on linguistic concepts utilizing one image paired with multiple captions; others adopt a visual or cross-modal perspective. > In this study, we are particularly interested in fine-grained visual concept understanding, which we believe is not covered in existing benchmarks in sufficient isolation. > Therefore, we create the InpaintCOCO dataset which consists of image pairs with minimum differences that lead to changes in the captions. Download the dataset: ```python from datasets import load_dataset dataset = load_dataset("phiyodr/inpaintCOCO") ``` ### Supported Tasks and Leaderboards InpaintCOCO is a benchmark to understand fine-grained concepts in multimodal models (vision-language) similar to [Winoground](https://huggingface.co/datasets/facebook/winoground). To our knowledge InpaintCOCO is the first benchmark, which consists of image pairs with minimum differences, so that the *visual* representation can be analyzed in a more standardized setting. ### Languages All texts are in English. ## Dataset Structure ```python DatasetDict({ test: Dataset({ features: ['concept', 'coco_caption', 'coco_image', 'inpaint_caption', 'inpaint_image', 'mask', 'worker', 'coco_details', 'inpaint_details', 'mask_details'], num_rows: 1260 }) }) ``` ### Data Instances An example looks as follows: ```python {'concept': 'object', 'coco_caption': 'A closeup of a large stop sign in the bushes.', 'coco_image': , 'inpaint_caption': 'A wooden bench in the bushes.', 'inpaint_image': , 'mask': , 'worker': 'k', 'coco_details': {'captions': ['A stop sign is shown among foliage and grass.', 'A close up of a Stop sign near woods. ', 'A closeup of a large stop sign in the bushes.', 'A large oval Stop sign near some trees.', 'a close up of a stop sign with trees in the background'], 'coco_url': 'http://images.cocodataset.org/val2017/000000252332.jpg', 'date_captured': '2013-11-17 08:29:48', 'flickr_url': 'http://farm6.staticflickr.com/5261/5836914735_bef9249442_z.jpg', 'height': 480, 'id': 252332, 'image_license': 'https://creativecommons.org/licenses/by/2.0/', 'text_license': 'https://creativecommons.org/licenses/by/4.0/legalcode', 'width': 640}, 'inpaint_details': {'duration': 18, 'guidance_scale': 7.5, 'num_inference_steps': 100, 'prompt': 'wooden bench', 'prompts_used': 2, 'quality': 'very good'}, 'mask_details': {'height_factor': 25, 'prompt': 'stop sign', 'prompts_used': 1, 'width_factor': 25}} ``` ## Dataset Creation > The challenge set was created by undergraduate student workers. They were provided with an interactive Python environment with which they interacted via various prompts and inputs. > The annotation proceeds as follows: The annotators are provided with an image and decide if the image is suitable for editing. If yes, they input the prompt for the object that should be replaced. Using the open vocabulary segmentation model [CLIPSeg](https://huggingface.co/CIDAS/clipseg-rd64-refined) ([Lüddecke and Ecker, 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Luddecke_Image_Segmentation_Using_Text_and_Image_Prompts_CVPR_2022_paper.html)) we obtain a mask for our object of interest (i.e., "fire hydrant"). Then, the annotator inputs a prompt for [Stable Diffusion v2 Inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) ([Rombach et al., 2022](https://ommer-lab.com/research/latent-diffusion-models/)) (e.g. with the prompt "yellow fire hydrant"), which shows three candidate images. The annotators can try new prompts or skip the current image if the result is insufficient. Finally, the annotator enters a new caption that matches the edited image. #### Source Data InpaintCOCO is based on MS COCO 2017 validation set ([image](http://images.cocodataset.org/zips/val2017.zip), [annotations](http://images.cocodataset.org/annotations/annotations_trainval2014.zip)). ``` @misc{lin2015microsoft, title={Microsoft COCO: Common Objects in Context}, author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár}, year={2015}, eprint={1405.0312}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Limitations > The images in the COCO dataset come from Flickr from 2014; therefore, they reflect the Flickr user structure at that time, i.e., the images mostly show the Western world and/or other countries from the Western perspective. The captions are in English. Thus, the model we developed does not generalize well beyond the Western world ## Licensing Information * Images come with individual licenses (`image_license`) based on their Flickr source. The possible licenses are * [CC BY-NC-SA 2.0 Deed](https://creativecommons.org/licenses/by-nc-sa/2.0/), * [CC BY-NC 2.0 Deed](https://creativecommons.org/licenses/by-nc/2.0/), * [CC BY 2.0 Deed](https://creativecommons.org/licenses/by/2.0/), and * [CC BY-SA 2.0 Deed](https://creativecommons.org/licenses/by-sa/2.0/). * The remaining work comes with the [CC BY 4.0 Legal Code](https://creativecommons.org/licenses/by/4.0/legalcode) license. ## Citation Information Our [InpaintCOCO](https://aclanthology.org/2024.alvr-1.9.pdf) dataset: ``` @misc{roesch2024enhancing, title={Enhancing Conceptual Understanding in Multimodal Contrastive Learning through Hard Negative Samples}, author={Philipp J. Rösch and Norbert Oswald and Michaela Geierhos and Jindřich Libovický}, year={2024}, eprint={2403.02875}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` For the MS COCO dataset please see above.