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8 results for “VQA”
Remote Sensing VQA - Low Resolution (RSVQA LR)
<p>Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task.</p> <p>This page concerns the low resolution dataset.</p>
Remote Sensing VQA - High Resolution (RSVQA HR)
<p>Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task.</p> <p>This page is about the high resolution dataset.</p>
Diabetic Macular Edema VQA Dataset
<p>Medical VQA dataset built from the <a href="https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid">IDRiD</a> and <a href="https://www.adcis.net/en/third-party/e-ophtha/">eOphta</a> datasets. The dataset contains both healthy and unhealthy fundus images. For each image, a set of pre-defined questions is generated, including questions about regions (e.g. are there hard exudates in this region?), for which an associated mask denotes the location of the region.</p> <p>The motivation for this dataset includes the lack of public medical VQA datasets with related questions. In our dataset, questions are related because there is a high-level question about the DME grade of the image, and associated low-level questions that can lead to the answer of the high-level question. This allows to study the consistency of a VQA model i.e. how often the model produces contradictory answers to questions about a given image. Questions about regions are also a novel feature of this dataset.</p> <p>The dataset can be used for general VQA purposes, and also for the more specific purpose of consistency improvement.</p> <p>Number of images : Train: 433 Val: 112 Test: 134</p> <p>Number of QA pairs: Train: 9779 Val: 2380 Test: 1311</p> <p>More details can be found <a href="https://github.com/sergiotasconmorales/consistency_vqa/blob/master/DATA.md">here</a>.</p> <p>If you use this dataset, please make sure you cite <a href="https://arxiv.org/abs/2206.13296">our paper</a>:</p> <p><em>@inproceedings{tascon2022consistency,<br> title={Consistency-Preserving Visual Question Answering in Medical Imaging},<br> author={Tascon-Morales, Sergio and Márquez-Neila, Pablo and Sznitman, Raphael},<br> booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},<br> pages={386--395},<br> year={2022},<br> organization={Springer}<br> }</em></p> <p>Do you need annotations about logical relations? No problem; check out our <a href="https://zenodo.org/record/7777849">DME VQA dataset with logical relations</a>.</p>
Localized Medical VQA
<p>This dataset contains 3 sub-datasets with questions about regions for the Medical Visual Question Answering (VQA) task. Traditionally, questions are asked about the entire image. In these datasets, we ask questions about randomly generated regions in an image for fundus images as well as cataract surgery frames and surgeries performed with the DaVinci robot.</p><p>All three datasets were created using publicly available datasets.</p><p>For more information and code, visit <a href="https://github.com/sergiotasconmorales/locvqa">our GitHub page.</a></p><p>If you use this dataset, please cite our work:</p><blockquote><p>@inproceedings{tascon2023localized, <br>title={Localized Questions in Medical Visual Question Answering}, <br>author={Tascon-Morales, Sergio and M{\'a}rquez-Neila, Pablo and Sznitman, Raphael}, <br>booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention}, <br>pages={361--370}, <br>year={2023}, <br>organization={Springer} <br>} </p></blockquote>
Diabetic Macular Edema VQA Dataset with Logical Relations
<p>Same as <a href="https://zenodo.org/record/6784358">Diabetic Macular Edema VQA Dataset</a>, but logical relations are added between pairs of questions.</p> <p>If you use this dataset, please cite our paper:</p> <pre>@inproceedings{tascon2023logical, title={Logical Implications for Visual Question Answering Consistency}, author={Tascon-Morales, Sergio and M{\'a}rquez-Neila, Pablo and Sznitman, Raphael}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={6725--6735}, year={2023} }</pre>
VQA-Introspect with Logical Relations
<p>This is a version of the <a href="https://www.microsoft.com/en-us/research/project/vqa-introspect/">VQA-Introspect dataset</a> by Selvaraju et al., but with added annotations about logical relations for binary QA pairs. Relations have been predicted using a fine-tuned BERT, which was pre-trained for NLI and fine-tuned on a sub-set of VQA-Introspect.</p> <p>In general, entries have the following fields:<br> img_id: This is the image name without extension (images come from COCO)<br> question_id: Question identifier as int<br> sent: String version of the question<br> question_type: Type of question (how it starts)<br> answer_type: Type of answer<br> label: Answers using soft scores (as required by LXMERT)<br> role: Question role (main, sub or unk)<br> Questions with role='sub' also have a field named parent, which indicates the ID of the QA pair it is related to, and a field named rel, which contains the relation to the parent.</p> <p>Total samples:<br> Train: 215862 (sub: 160085, main: 55777)<br> Val: 69668 (sub: 49882, main: 19786)</p> <p>Images must be downloaded separately from the <a href="https://cocodataset.org/#download">COCO Dataset website</a>.</p> <p> </p> <p>If you use this dataset, please cite:</p> <pre>@inproceedings{tascon2023logical, title={Logical Implications for Visual Question Answering Consistency}, author={Tascon-Morales, Sergio and M{\'a}rquez-Neila, Pablo and Sznitman, Raphael}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={6725--6735}, year={2023} }</pre> <p>as well as the original publication where VQA-Introspect was presented:</p> <pre>@inproceedings{selvaraju2020squinting, title={Squinting at vqa models: Introspecting vqa models with sub-questions}, author={Selvaraju, Ramprasaath R and Tendulkar, Purva and Parikh, Devi and Horvitz, Eric and Ribeiro, Marco Tulio and Nushi, Besmira and Kamar, Ece}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={10003--10011}, year={2020} } </pre> <p>Note: We respect the original <a href="https://msropendata-web-api.azurewebsites.net/licenses/f1f352a6-243f-4905-8e00-389edbca9e83/view">license terms</a> of the VQA-Introspect dataset and make manifest that the liability warranty described in those terms (§ 4.2 and 4.3) apply to this dataset too.</p>
VQA_dataset_French
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Path-VQA
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