Applying Vector Symbolic Architecture and Semiotic Approach to Visual Dialog

Kovalev, Alexey K.; Shaban, Makhmud; Chuganskaya, Anfisa A.; Panov, Aleksandr I.

doi:10.1007/978-3-030-86271-8_21

Cited by 2 publications

(1 citation statement)

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“…In [202], it was applied to a dataset constructed to facilitate visual navigation in human-centered environments. This approach was further extended from Visual Question Answering to Visual Dialog in [232].…”

Section: Computer Vision and Scenementioning

confidence: 99%

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

et al. 2023

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This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [322, 327] is an influential HDC/VSA model that is well-known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field. Part I of this survey [223] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain, however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners.

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Section: Computer Vision and Scenementioning

confidence: 99%