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3 results for “Visual Question Answering”
Toloka Visual Question Answering Dataset
<p>Our dataset consists of the images associated with textual questions. One entry (instance) in our dataset is a question-image pair labeled with the ground truth coordinates of a bounding box containing the visual answer to the given question. The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in Context dataset, <a href="https://cocodataset.org/">MS COCO</a>. All data labeling was performed on the Toloka crowdsourcing platform, <a href="https://toloka.ai/">https://toloka.ai/</a>.</p> <p>Our dataset has 45,199 instances split among three subsets: <strong>train</strong> (38,990 instances), <strong>public test</strong> (1,705 instances), and <strong>private test</strong> (4,504 instances). The entire train dataset was available for everyone since the start of the challenge. The public test dataset was available since the evaluation phase of the competition, but without any ground truth labels. After the end of the competition, public and private sets were released.</p> <p>The datasets will be provided as files in the comma-separated values (CSV) format containing the following columns.</p> <table> <tbody> </tbody> </table> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Type</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>image</td> <td>string</td> <td>URL of an image on a public content delivery network</td> </tr> <tr> <td>width</td> <td>integer</td> <td>image width</td> </tr> <tr> <td>height</td> <td>integer</td> <td>image height</td> </tr> <tr> <td>left</td> <td>integer</td> <td>bounding box coordinate: left</td> </tr> <tr> <td>top</td> <td>integer</td> <td>bounding box coordinate: top</td> </tr> <tr> <td>right</td> <td>integer</td> <td>bounding box coordinate: right</td> </tr> <tr> <td>bottom</td> <td>integer</td> <td>bounding box coordinate: bottom</td> </tr> <tr> <td>question</td> <td>string</td> <td>question in English</td> </tr> </tbody> </table> <p>This upload also contains a ZIP file with the images from MS COCO.</p>
Amharic visual question answering on Ethiopian tourism
<p>Visual Question Answering (VQA) is a Vision-to-Text (V2T) task that integrates visual <br>features of images with natural language questions to generate meaningful responses. <br>Most existing research has focused on English, leaving a significant gap for other <br>languages, including Amharic. Tourism, a major global industry, relies heavily on <br>interactions where visitors seek information about natural, historical, cultural, and <br>religious sites. Ethiopia is a remarkable tourist destination, home to unique sites such as <br>the Rock-hewn churches of Lalibela and the Castles of Gondar, as well as natural <br>phenomena like Simien National Park and Lake Tana. Most visitors are local, creating an <br>urgent need for a VQA model that can deliver accurate, culturally relevant information in <br>Amharic. Unfortunately, no such model currently exists to assist tourists at these heritage <br>sites. This research addresses this gap by developing an Amharic Visual Question <br>Answering model specifically tailored for Ethiopian tourism. A new Amharic VQA <br>dataset was created using 2,200 diverse images from Ethiopian tourist sites paired with <br>6,600 questions in Amharic, covering natural landmarks, historical sites, and religious <br>celebrations. Our dataset is collected from various sources, including the UNICCO <br>website, the Amhara Tourism office, and online platforms such as Facebook, Free pixel, <br>and Instagram. Each image is complemented by three corresponding questions <br>formulated by three individual experts and answered by ten candidates. The questions, <br>answers, and images are linked through annotations and fed into the model. We used <br>ResNet-50 for feature extraction and Bidirectional Gated Recurrent Unit (BiGRU) with <br>attention mechanisms, achieving a testing accuracy of 54.98%, demonstrating the model's <br>effectiveness in answering questions about Ethiopian heritage. We will expand this <br>research using external knowledge to gat answer and description beyond image and <br>custom object detection</p>
PharmaVQA: A Retrieval-Augmented Visual Question Answering Framework for Molecular Representation via Pharmacophores Guided Prompts
<p>The dataset includes the preprocessed Li's dataset, MoleculeACE dataset, and 3 ligands dataset.</p>
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