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Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering

arXiv 2306.04938 ↗First posted 8 Jun 2023cs.CV cs.AIcited by 0

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Abstract (arXiv)

Visual question answering (VQA) is a Multidisciplinary research problem that pursued through practices of natural language processing and computer vision. Visual question answering automatically answers natural language questions according to the content of an image. Some testing questions require external knowledge to derive a solution. Such knowledge-based VQA uses various methods to retrieve features of image and text, and combine them to generate the answer. To generate knowledgebased answers either question dependent or image dependent knowledge retrieval methods are used. If knowledge about all the objects in the image is derived, then not all knowledge is relevant to the question. On other side only question related knowledge may lead to incorrect answers and over trained model that answers question that is irrelevant to image. Our proposed method takes image attributes and question features as input for knowledge derivation module and retrieves only question relevant knowledge about image objects which can provide accurate answers.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 8 Jun 2023.

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References · 15

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3 more
  1. 2014word2vec Parameter Learning Explainedcited by 53
  2. 2015Ask Your Neurons: A Neural-based Approach to Answering Questions about Imagescited by 51
  3. 2019Light-Weight RetinaNet for Object Detectioncited by 11

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