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primary topic: Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential

Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential
type: AcademicArticle
type: Article
abstract: Generative intelligence relies heavily on the integration of vision and language. Much of the research has focused on image captioning, which involves describing images with meaningful sentences. Typically, when generating sentences that describe the visual content, a language model and a vision encoder are commonly employed. Because of the incorporation of object areas, properties, multi-modal connections, attentive techniques, and early fusion approaches like bidirectional encoder representations from transformers (BERT), these components have experienced substantial advancements over the years. This research offers a reference to the body of literature, identifies emerging trends in an area that blends computer vision as well as natural language processing in order to maximize their complementary effects, and identifies the most significant technological improvements in architectures employed for image captioning. It also discusses various problem variants and open challenges. This comparison allows for an objective assessment of different techniques, architectures, and training strategies by identifying the most significant technical innovations, and offers valuable insights into the current landscape of image captioning research.
date: 2024-02
is part of: IEEE Access
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential (Texto)
has document: Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential (Otro)
subject: Engineering

Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential (Texto)
type: Document
has file: Deep_Learning_Approaches_for_Image_Captioning_Opportunities_Challenges_and_Future_Potential.pdf
has part: Deep_Learning_Approaches_for_Image_Captioning_Opportunities_Challenges_and_Future_Potential.pdf

IEEE Access
type: Collection
issn: 21693536

Saif Ur Rehman
type: Person
given name: Saif Ur
family name: Rehman

Thomas Prola
type: Person
given name: Thomas
family name: Prola

Khalid Mahmood
type: Person
given name: Khalid
family name: Mahmood

Azhar Jamil
type: Person
given name: Azhar
family name: Jamil

Mónica Gracia Villar
type: Person
given name: Mónica
family name: Gracia Villar

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Md Abdus Samad
type: Person
given name: Md Abdus
family name: Samad

Isabel De La Torre Diez
type: Person
given name: Isabel De La Torre
family name: Diez

Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential (Otro)
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Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential (Otro)
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Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential (Otro)
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Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential (Otro)
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Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential (Otro)
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HTML Summary of #11065 Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential
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primary topic: Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential

Engineering
type: Concept
preferred label: Ingeniería @es