Image and Video Caption Generation

Harshitha Loganathan , Manju Bhashini. D , Grace Elizabeth , Sangeetha Krishnan , Salini. R

CNN, deep learning, LSTM, RNN, image captioning, video captioning, attention

This consideration distinguishes different strategies for building encoder-decoder systems based on profound learning that gives full normal dialect caption of video arrangements. Recurrent Neural Network (RNNs) examines the data provided by the details in the image to produce a logical, sequential representation of the image. This study also provides evaluation criteria for determining the efficiency of video captioning models with different datasets used for image and video captions. Deep- learning based approaches are currently used for video processing, but this paper looks into the various methods for generating natural language.

Published Paper   E-Certificate

"Image and Video Caption Generation", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.196-210, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001031.pdf

Volume 11 Issue 2, February-2024

Pages : 196-210

Paper Reg. ID: TIJER_150991

Published Paper Id: TIJERB001031

Downloads: 000284

Research Area: Science and Technology

Country: THIRUVALLUR, TAMILNADU, India

Published Paper PDF: https://tijer.org/TIJER/papers/TIJERB001031

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJERB001031

ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: TIJER (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition