OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
Visual Question Answering (VQA) in its ideal form lets us study reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. However, most VQA benchmarks to date are focused on questions such as simple counting, visual attributes, and object detection that do not require reasoning or knowledge beyond what is in the image. In this paper, we address the task of knowledge-based visual question answering and provide a benchmark, called OK-VQA, where the image content is not sufficient to answer the questions, encouraging methods that rely on external knowledge resources. Our new dataset includes more than 14,000 questions that require external knowledge to answer. We show that the performance of the state-of-the-art VQA models degrades drastically in this new setting. Our analysis shows that our knowledge-based VQA task is diverse, difficult, and large compared to previous knowledge-based VQA datasets. We hope that this dataset enables researchers to open up new avenues for research in this domain. See http://okvqa.allenai.org to download and browse the dataset.
Code (1)
Tasks
object-detectionObject DetectionQuestion AnsweringScene UnderstandingVisual Question AnsweringVisual Question Answering (VQA)Similar Papers 제목 키워드 기반
ReasonVQA: A Multi-hop Reasoning Benchmark with Structural Knowledge for Visual Question Answering
In this paper, we propose a new dataset, ReasonVQA, for the Visual Question Answering (VQA) task. Our dataset is automatically integrated with structured encyclopedic knowledge and constructed using a low-cost framework,…
Visual Question AnsweringQuery and Attention Augmentation for Knowledge-Based Explainable Reasoning
Explainable visual question answering (VQA) models have been developed with neural modules and query-based knowledge incorporation to answer knowledge-requiring questions. Yet, most reasoning methods cannot effective…
Question AnsweringVisual Question AnsweringVisual Question Answering (VQA)A Simple Baseline for Knowledge-Based Visual Question Answering
This paper is on the problem of Knowledge-Based Visual Question Answering (KB-VQA). Recent works have emphasized the significance of incorporating both explicit (through external databases) and implicit (through LLMs) kn…
In-Context LearningQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)Image Captioning for Effective Use of Language Models in Knowledge-Based Visual Question Answering
Integrating outside knowledge for reasoning in visio-linguistic tasks such as visual question answering (VQA) is an open problem. Given that pretrained language models have been shown to include world knowledge, we propo…
Image CaptioningKnowledge GraphsQuestion AnsweringVisual Question Answering+2Image Captioning and Visual Question Answering Based on Attributes and External Knowledge
Much recent progress in Vision-to-Language problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-l…
General KnowledgeImage CaptioningQuestion AnsweringVisual Question Answering+1