Multimodal Graph Networks for Compositional Generalization in Visual Question Answering

By Raeid Saqur, Karthik Narasimhan. 2020

61 citations recorded by the source corpus.

Subfields: Multimodal Learning, Graph Learning

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Citations in the corpus

Grammar-Based Grounded Lexicon Learning (2022, methodology): “There are two representative approaches: building structured neural networks with explicit phrase-based structures or segments [37, 51, 41, 34]; and using data augmentation techniques [3, 17, 2].”
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition (2022, background): “Existing works have used these methods in varied application domains: toy programming tasks (Bošnjak et al., 2017; Cai et al., 2017; Bunel et al., 2018; Pierrot et al., 2019), VQA (Andreas et al., 2016; Saqur and Narasimhan, 2020; Pahuja et al., 2019; Hu et al., 2017; Johnson et al., 2017; Gupta et ”
Large Language Models as Commonsense Knowledge for Large-Scale Task Planning (2023, background): “It is useful in a broad range of tasks, such as language-conditioned manipulation [9, 10, 11, 12, 13, 14, 15], visual language navigation [16, 17], and visual question answering [18, 19, 20, 21, 22, 23, 24].”
Multi-level Feature Learning for Contrastive Multi-view Clustering (2021, unknown): “Multi-view clustering (MVC) is attracting more and more attention in recent years [22,50,52,57] as multi-view data or multi-modal data can provide common semantics to improve the learning effectiveness [3,14,27,33,36,43].”
Modular Visual Question Answering via Code Generation (2023, background): “Modular approaches, on the other hand – from classic methods (Krishnamurthy and Kollar, 2013), to differentiable neural module networks (NMNs) (Andreas et al., 2016; Hu et al., 2017; Saqur and Narasimhan, 2020)) – offer a potential route to leverage and scale to the compositional nature of visual re”
Deep Multimodal Data Fusion (2024, background): “There are various other multimodal tasks, e.g., visual question answering ( VQA ) (text-image: [154, 220], text-video: [82, 223]), RGB-depth object segmentation [31, 39], medical data analysis [181, 185], and image captioning [216, 237].”