Combining Text and Image Analysis Methods for Solving Multimodal Classification Problems

Andriyanov, Nikita

doi:10.1134/s1054661822030026

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…In fact, the recognition memory gap between object names validated with visual vs. nonvisual attributes disappeared when the word (apple) was employed as the test item rather than the image. The [31] adapted paradigm to isolate the processes of text comprehension and image recognition. In the research phase, they discovered that people remembered more information when the photos' object orientations and shapes matched the sentence's indicated object orientations and shapes.…”

Section: Embodiment and Knowledge Representationmentioning

confidence: 99%

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

Akshaya¹,

Bizotto

Sathiyanarayanan

2023

JMC

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Latent Semantic Analysis (LSA) is an approach used for expressing and extracting textual meanings using statistical evaluations or modeling applied to vast corpora of text, and its development has been a major motivation for this study to understand the design thinking approach. We introduced LSA and gave some instances of how it might be used to further our knowledge of cognition and to develop practical technology. Since LSA's inception, other alternative statistical models for meaning detection and analysis in text corpora have been created, tested, and refined. This study demonstrates the value that statistical models of semantics provide to the study of cognitive science and the development of cognition. These models are particularly useful because they enable researchers to study a wide range of problems pertaining to knowledge, discourse perception, text cognition, and language using expansive representations of human intelligence.

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Section: Embodiment and Knowledge Representationmentioning

confidence: 99%

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

Akshaya¹,

Bizotto

Sathiyanarayanan

2023

JMC

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“…AGI is characterized by an algorithm or set of algorithms capable of performing tasks across multiple domains as a typical human being would [1]. Creating an artificial general intelligence (AGI) requires a qualitative analysis of heterogeneous information, which is unique to humans' ability to make intelligent decisions based on vision, hearing, reading, and other senses [2]. The multimodal nature of data makes it possible to obtain highquality solutions to problems of analyzing corrupted or visually attacked images, provided that additional, nonvisual information is available.…”

Section: Artificial General Intelligence (Agi)mentioning

confidence: 99%

Qualitative Data Analysis in the Age of Artificial General Intelligence

Abdüsselam

2023

IJANSER

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Artificial General Intelligence (AGI) is a rapidly developing field in the domain of artificial intelligence. AGI systems aim to replicate human-like intelligence and adaptability by possessing the capacity to perform a variety of intellectual tasks that are commonly associated with human beings. As opposed to narrow or weak AI systems, which are designed to perform specific tasks or solve particular problems, AGI seeks to generate machines that can reason, learn, and solve problems with the same level of competence and flexibility as humans. The multimodal nature of data makes it possible to obtain high-quality solutions to problems of analyzing corrupted or visually attacked images, provided that additional, nonvisual information is available. Additionally, the trend in artificial intelligence towards models with billions of parameters is due to the growth of data modality, leading to significantly higher complexity of models. The paper discusses the field of Artificial General Intelligence (AGI) and its potential to replicate human-like intelligence and adaptability. AGI systems aim to perform a variety of intellectual tasks that are commonly associated with human beings. Overall, the paper provides insights into the current state and future prospects of AGI research, highlighting both the potential and challenges of this rapidly developing field.

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Combining Computer Vision and Word Processing to Classify Film Genres

Andriyanov

Dementiev

2023

2023 25th International Conference on Digital Signal Processing and Its Applications (DSPA)

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Combining Text and Image Analysis Methods for Solving Multimodal Classification Problems

Cited by 5 publications

References 11 publications

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

Human Intelligence and Value of Machine Advancements in Cognitive Science A Design thinking Approach

Qualitative Data Analysis in the Age of Artificial General Intelligence

Combining Computer Vision and Word Processing to Classify Film Genres

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