.png){kind=icon}
返回
Lighting Search Algorithm With Convolutional Neural Network-Based Image Captioning System for Natural Language Processing
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3342703.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, deep learning models have become more prominent due to their tremendous performance for real-time tasks like face recognition, object detection, natural language processing (NLP), instance segmentation, image classification, gesture recognition, and video classification. Image captioning is one of the critical tasks in NLP and computer vision (CV). It completes conversion from image to text; specifically, the model produces description text automatically based on the input images. In this aspect, this article develops a Lighting Search Algorithm (LSA) with a Hybrid Convolutional Neural Network Image Captioning System (LSAHCNN-ICS) for NLP. This introduced LSAHCNN-ICS system develops an end-to-end model which employs convolutional neural network (CNN) based ShuffleNet as an encoder and HCNN as a decoder. At the encoding part, the ShuffleNet model derives feature descriptors of the image. Besides, in the decoding part, the description of text can be generated using the proposed hybrid convolutional neural network (HCNN) model. To achieve improved captioning results, the LSA is applied as a hyperparameter tuning strategy, representing the innovation of the study. The simulation analysis of the presented LSAHCNN-ICS technique is performed on a benchmark database, and the obtained results demonstrated the enhanced outcomes of the LSAHCNN-ICS algorithm over other recent methods with maximum Consensus-based Image Description Evaluation (CIDEr Code) of 43.60, 59.54, and 135.14 on Flickr8k, Flickr30k, and MSCOCO datasets correspondingly.
Keyword:
Feature extraction
Convolutional neural networks
Visualization
Decoding
Natural language processing
Tuning
Deep convolutional neural network
natural language processing
image captioning
machine learning
hyperparameter tuning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Investigation of fast processes in condensed matter by time-resolved x-ray diffraction通过时间分辨X射线衍射研究凝聚态物质中的快速过程
PREAI
分享收藏Estudiantes con discapacidad en una universidad chilena: desafíos de la inclusión智利大学中的残疾学生：包容的挑战
OAAI
OAAI分享收藏分享收藏
查看更多内容