Sequence-to-sequence image caption generator

2019 ◽  
Author(s):  
Rehab Alahmadi ◽  
Chung Hyuk Park ◽  
James Hahn
Keyword(s):  
Image Caption

Image Caption Generation Using Deep Learning

2018 ◽  
Vol 06 (10) ◽  
pp. 53-55
Author(s):  
Sailee P. Pawaskar ◽  
J. A. Laxminarayana
Keyword(s):  
Deep Learning ◽  
Image Caption Generation ◽  
Image Caption

Show, Rethink, And Tell: Image Caption Generation With Hierarchical Topic Cues

2021 ◽  
Author(s):  
Feng Chen ◽  
Songxian Xie ◽  
Xinyi Li ◽  
Jintao Tang ◽  
Kunyuan Pang ◽  
...  
Keyword(s):  
Image Caption Generation ◽  
Image Caption

scholarly journals VSAM-Based Visual Keyword Generation for Image Caption

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 27638-27649
Author(s):  
Suya Zhang ◽  
Yana Zhang ◽  
Zeyu Chen ◽  
Zhaohui Li
Keyword(s):  
Image Caption

Improved Framework using Rider Optimization Algorithm for Precise Image Caption Generation

2021 ◽  
pp. 2250021
Author(s):  
Chaitrali Prasanna Chaudhari ◽  
Satish Devane
Keyword(s):  
Language Processing ◽  
Optimization Algorithm ◽  
Short Term Memory ◽  
Activation Function ◽  
Input Image ◽  
Image Captioning ◽  
Word Generation ◽  
Adopted Model ◽  
Conventional Models ◽  
Image Caption

“Image Captioning is the process of generating a textual description of an image”. It deploys both computer vision and natural language processing for caption generation. However, the majority of the image captioning systems offer unclear depictions regarding the objects like “man”, “woman”, “group of people”, “building”, etc. Hence, this paper intends to develop an intelligent-based image captioning model. The adopted model comprises of few steps like word generation, sentence formation, and caption generation. Initially, the input image is subjected to the Deep learning classifier called Convolutional Neural Network (CNN). Since the classifier is already trained in the relevant words that are related to all images, it can easily classify the associated words of the given image. Further, a set of sentences is formed with the generated words using Long-Short Term Memory (LSTM) model. The likelihood of the formed sentences is computed using the Maximum Likelihood (ML) function, and the sentences with higher probability are taken, which is further used for generating the visual representation of the scene in terms of image caption. As a major novelty, this paper aims to enhance the performance of CNN by optimally tuning its weight and activation function. This paper introduces a new enhanced optimization algorithm Rider with Randomized Bypass and Over-taker update (RR-BOU) for this optimal selection. In the proposed RR-BOU is the enhanced version of the Rider Optimization Algorithm (ROA). Finally, the performance of the proposed captioning model is compared over other conventional models with respect to statistical analysis.

The Accurate Guidance for Image Caption Generation

2018 ◽  
pp. 15-26
Author(s):  
Xinyuan Qi ◽  
Zhiguo Cao ◽  
Yang Xiao ◽  
Jian Wang ◽  
Chao Zhang
Keyword(s):  
Image Caption Generation ◽  
Image Caption

The Design Patent Images Classification Based on Image Caption Model

2020 ◽  
pp. 353-362
Author(s):  
Hongyu Liu ◽  
Qingyun Dai ◽  
Ya Li ◽  
Chuxin Zhang ◽  
Siyu Yi ◽  
...  
Keyword(s):  
Design Patent ◽  
Image Caption

Regenerating Image Caption with High-Level Semantics

2020 ◽  
pp. 74-86
Author(s):  
Wei-Dong Tian ◽  
Nan-Xun Wang ◽  
Yue-Lin Sun ◽  
Zhong-Qiu Zhao
Keyword(s):  
High Level ◽  
Image Caption
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