Image Captioning and Visual Question Answering Based on Attributes and Their Related External Knowledge

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End-to-End Concept Word Detection for Video Captioning, Retrieval, and Question Answering (2016, methodology): “We employ a convolutional neural network (CNN) to encode video input.”
Creativity: Generating Diverse Questions Using Variational Autoencoders (2017, background): “When considering generation of text from images, caption and paragraph generation [32, 4, 11, 18, 19, 20, 13, 33, 37, 40, 48, 60, 64, 69], as well as visual question answering [2, 22, 47, 52, 57, 67, 68, 71, 21, 34, 74, 1, 15, 31, 72, 73, 66, 46, 45, 31] come to mind immediately.”
Attend and Imagine: Multi-Label Image Classification With Visual Attention and Recurrent Neural Networks (2019, methodology): “The CNN-RNN based method benefits by the development of image captioning [39], [51]–[53].”
Zero-Shot Visual Question Answering (2016, methodology): “This distinction reﬂects the fact that CNNs pre-trained on ImageNet [4] are commonly used in existing VQA methods, and the fact that VQA is the task that we are actually interested in.”
Straight to the Facts: Learning Knowledge Base Retrieval for Factual Visual Question Answering (2018, methodology): “Most of the methods developed for visual question answering [2,8,10,18,19,20,21,22,23,24,12,27,29,30,31,6,7,32,33,34] focus exclusively on answering questions related to observed content.”