Visual Questioner using Bert Transformer

International Journal of Science and Management Studies (IJSMS)
© 2024 by IJSMS Journal
Volume-7 Issue-1
Year of Publication : 2024
Authors : Dr.Visu P, Surya Narayanan K, Syed Nafeez Ahmed A, Aravind Kumar S
DOI: 10.51386/25815946/ijsms-v7i1p113
MLA Style: Dr.Visu P, Surya Narayanan K, Syed Nafeez Ahmed A, Aravind Kumar S "Visual Questioner using Bert Transformer" International Journal of Science and Management Studies (IJSMS) V7.I1 (2024): 83-89.

APA Style: Dr.Visu P, Surya Narayanan K, Syed Nafeez Ahmed A, Aravind Kumar S, Visual Questioner using Bert Transformer, International Journal of Science and Management Studies (IJSMS), v7(i1), 83-89.
The Visual Questioner, an innovative machine learning endeavor, represents a cutting-edge project designed to process images and facilitate user interaction through AI-generated responses to inquiries. Focused on the fusion of visual recognition and natural language understanding, the overarching objective of this project is to cultivate a robust model capable of discerning intricate details within an image and, subsequently, formulating coherent textual responses to user-generated questions. At the core of the Visual Questioner lies the imperative to bridge the semantic gap between visual content and linguistic expression, enhancing the depth of comprehension and interaction within the artificial intelligence paradigm. By harnessing advanced algorithms and neural network architectures, this initiative aspires to elevate the sophistication of image-based question-answering systems, presenting a pivotal advancement in the realm of human-machine communication. This research not only delves into the technical intricacies of image understanding but also underscores the imperative role of natural language generation, thereby contributing substantively to the evolving landscape of AI applications. As we navigate through the complexities of this project, its potential implications span diverse domains, ranging from human-computer interaction to autonomous systems and beyond, underscoring the multifaceted significance of the Visual Questioner in contemporary artificial intelligence research.
Keywords: Machine Learning, Visual Recognition, Natural Language Understanding, Image Based Question Answering Systems.
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