Open AccessPosted Content
VQA: Visual Question Answering
Aishwarya Agrawal,Jiasen Lu,Stanislaw Antol,Margaret Mitchell,C. Lawrence Zitnick,Dhruv Batra,Devi Parikh +6 more
TLDR
The task of free-form and open-ended Visual Question Answering (VQA) is proposed, given an image and a natural language question about the image, the task is to provide an accurate natural language answer.Abstract:
We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing ~0.25M images, ~0.76M questions, and ~10M answers (this http URL), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance. Our VQA demo is available on CloudCV (this http URL).read more
Citations
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Journal ArticleDOI
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Ranjay Krishna,Yuke Zhu,Oliver Groth,Justin Johnson,Kenji Hata,Joshua Kravitz,Stephanie Chen,Yannis Kalantidis,Li-Jia Li,David A. Shamma,Michael S. Bernstein,Li Fei-Fei +11 more
TL;DR: The Visual Genome dataset as mentioned in this paper contains over 108k images where each image has an average of $35$35 objects, $26$26 attributes, and $21$21 pairwise relationships between objects.
Journal ArticleDOI
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta,Natalia Díaz-Rodríguez,Javier Del Ser,Javier Del Ser,Adrien Bennetot,Adrien Bennetot,Siham Tabik,Alberto Barbado,Salvador García,Sergio Gil-Lopez,Daniel Molina,Richard Benjamins,Raja Chatila,Francisco Herrera +13 more
TL;DR: In this paper, a taxonomy of recent contributions related to explainability of different machine learning models, including those aimed at explaining Deep Learning methods, is presented, and a second dedicated taxonomy is built and examined in detail.
Posted Content
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering.
TL;DR: A combined bottom-up and top-down attention mechanism that enables attention to be calculated at the level of objects and other salient image regions is proposed, demonstrating the broad applicability of this approach to VQA.
Proceedings ArticleDOI
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
TL;DR: The ChestX-ray dataset as discussed by the authors contains 108,948 frontal-view X-ray images of 32,717 unique patients with the text-mined eight disease image labels from the associated radiological reports using natural language processing.
Proceedings ArticleDOI
Stacked Attention Networks for Image Question Answering
TL;DR: In this paper, a stacked attention network (SAN) is proposed to learn to answer natural language questions from images by using semantic representation of a question as query to search for the regions in an image that are related to the answer.
References
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