c-RNN: A Fine-Grained Language Model for Image Captioning

Huang, Gengshi; Hu, Haifeng

doi:10.1007/s11063-018-9836-2

Cited by 16 publications

(3 citation statements)

References 11 publications

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“…The RNN-based reinforcement learning framework is proposed by integrating with a novel multi-level policy function (word-level policy and sentence-level polity) and multi-level reward function (visionlanguage reward and language-language reward) in [25]. The language model for image captioning, in [26], namely character-level RNN (c-RNN), is developed by composing the descriptive sentence with characterization. c-RNN model is based on the inject model that one of encoder-decoder architecture by substituting with character level instead of wordlevel image captioning model.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Two-Tier LSTM Model for Image Caption Generation

Khaing¹,

Yu²

2021

IJIES

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Image captioning is systematically generating the caption of the image with a sentence description. In the past few years, the automatic process of creating image caption has fascinated the great interest in Artificial Intelligence (AI) field. Image captioning defines as the basic process of building the conjunction of image processing and natural language processing at input and output position. All image processing tasks, such as the segmentation of image, object tracking, object detection, image recognition, and many others, are mostly performed using Convolutional Neural Networks (CNNs). To perform the natural language processing tasks, just as semantic role labelling, neural machine translation, speech recognition, question and answering, and many others, Recurrent Neural Networks (RNNs) and long-term memory networks (LSTMs) are essential for some of the biggest breakthroughs. This paper proposes the efficient encoder-decoder framework-based image captioning model, namely Two-Tier LSTM (TT-LSTM) Model. The TT-LSTM model is designedly implemented upon the encoder-decoder framework with two LSTM layers. This research is implemented on the MSCOCO, Flickr30k, and Flickr8k datasets; and evaluated with standard evaluation matrixes such as ROUGE-L, CIDEr, and four BLEU scores. The outcomes of the experiments on the typical datasets reveal that the proposed model generates meaningful natural language sentences. The proposed model also improves the sentence generation efficiency and can achieve better performance for image caption generation.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Two-Tier LSTM Model for Image Caption Generation

Khaing¹,

Yu²

2021

IJIES

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“…Some works have sought to build character-level models. For example, a character-level model with Recurrent Neural Network (RNN) is presented in [11]. This model reasons about word spelling and grammar dynamically.…”

Section: Language Modellingmentioning

confidence: 99%

“…1. A total of 8,515 articles scraped from Digikala online magazine 11 . This dataset includes seven different classes.…”

Section: Text Classificationmentioning

confidence: 99%

ParsBERT: Transformer-based Model for Persian Language Understanding

Farahani,

Gharachorloo,

Farahani

et al. 2020

Preprint

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The surge of pre-trained language models has begun a new era in the field of Natural Language Processing (NLP) by allowing us to build powerful language models. Among these models, Transformer-based models such as BERT have become increasingly popular due to their state-of-the-art performance. However, these models are usually focused on English, leaving other languages to multilingual models with limited resources. This paper proposes a monolingual BERT for the Persian language (ParsBERT), which shows its state-ofthe-art performance compared to other architectures and multilingual models. Also, since the amount of data available for NLP tasks in Persian is very restricted, a massive dataset for different NLP tasks as well as pretraining the model is composed. ParsBERT obtains higher scores in all datasets, including existing ones as well as composed ones and improves the state-of-the-art performance by outperforming both multilingual BERT and other prior works in Sentiment Analysis, Text Classification and Named Entity Recognition tasks.

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A novel automatic image caption generation using bidirectional long-short term memory framework

Khan

Naqvi

et al. 2021

Multimed Tools Appl

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c-RNN: A Fine-Grained Language Model for Image Captioning

Cited by 16 publications

References 11 publications

Two-Tier LSTM Model for Image Caption Generation

Two-Tier LSTM Model for Image Caption Generation

ParsBERT: Transformer-based Model for Persian Language Understanding

A novel automatic image caption generation using bidirectional long-short term memory framework

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