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Unanswerable Questions About Images and Texts
Frontiers in Artificial Intelligence, 2020 Questions about a text or an image that cannot be answered raise distinctive issues for an AI. This note discusses the problem of unanswerable questions in VQA (visual question answering), in QA (textual question answering), and in AI generally.
Ernest Davis
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Visual Storytelling with Question-Answer Plans
Findings of the Association for Computational Linguistics: EMNLP 2023, 2023 Visual storytelling aims to generate compelling narratives from image sequences. Existing models often focus on enhancing the representation of the image sequence, e.g., with external knowledge sources or advanced graph structures. Despite recent progress, the stories are often repetitive, illogical, and lacking in detail.
Liu, Danyang +2 more
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A Metamorphic Testing Approach for Assessing Question Answering Systems
Mathematics, 2021 Question Answering (QA) enables the machine to understand and answer questions posed in natural language, which has emerged as a powerful tool in various domains. However, QA is a challenging task and there is an increasing concern about its quality.
Kaiyi Tu, Mingyue Jiang, Zuohua Ding
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International Journal of Advanced Research in Science, Communication and Technology, 2022 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.
Qi Wu 0001 +4 more
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Multi-Shared Attention with Global and Local Pathways for Video Question Answering [PDF]
Jisuanji kexue, 2021 Video question answering is a challenging task of significant importance toward visual understanding.However,current visual question answering (VQA) methods mainly focus on a single static image,which is distinct from the sequential visual data we faced ...
WANG Lei-quan, HOU Wen-yan, YUAN Shao-zu, ZHAO Xin, LIN Yao, WU Chun-lei
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Learning Answer Embeddings for Visual Question Answering [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 We propose a novel probabilistic model for visual question answering (Visual QA). The key idea is to infer two sets of embeddings: one for the image and the question jointly and the other for the answers. The learning objective is to learn the best parameterization of those embeddings such that the correct answer has higher likelihood among all ...
Hexiang Hu, Wei-Lun Chao, Fei Sha
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Question Relevance in Visual Question Answering
CoRR, 2018 Free-form and open-ended Visual Question Answering systems solve the problem of providing an accurate natural language answer to a question pertaining to an image. Current VQA systems do not evaluate if the posed question is relevant to the input image and hence provide nonsensical answers when posed with irrelevant questions to an image. In this paper,
Prakruthi Prabhakar +2 more
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Improving reasoning with contrastive visual information for visual question answering
Electronics Letters, 2021 Visual Question Answering (VQA) aims to output a correct answer based on crossâmodality inputs including question and visual content. In general pipeline, information reasoning plays the key role for a reasonable answer.
Yu Long +3 more
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Survey of Text-based Visual Question Answering [PDF]
Jisuanji gongchengTraditional Visual Question Answering(VQA)only focuses on the visual object information in the image, ignoring the text information in the image. In addition to visual information, Text-based Visual Question Answering (TextVQA)also focuses on the text ...
Guide ZHU, Hai HUANG
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iVQA: Inverse Visual Question Answering [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 We propose the inverse problem of Visual question answering (iVQA), and explore its suitability as a benchmark for visuo-linguistic understanding. The iVQA task is to generate a question that corresponds to a given image and answer pair. Since the answers are less informative than the questions, and the questions have less learnable bias, an iVQA model
Liu, Feng, +4 more
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