Video captioning in Vietnamese using deep learning

Phuc, Dang Thi; Trieu, Tran Quang; Tính, Nguyễn Văn; Hieu, Dau Sy

doi:10.11591/ijece.v12i3.pp3092-3103

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…), the result is shown in Table 4. We use Nguyen's dataset [38], [39] including 18,108 questions and paragraphs for the improved BM25 algorithm. The results of comparisons between the two models are shown in Table 5.…”

Section: Results and Analysismentioning

confidence: 99%

Apply deep learning to improve the question analysis model in the Vietnamese question answering system

Phuc

Nghiem²,

Minh³

et al. 2023

IJECE

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Question answering (QA) system nowadays is quite popular for automated answering purposes, the meaning analysis of the question plays an important role, directly affecting the accuracy of the system. In this article, we propose an improvement for question-answering models by adding more specific question analysis steps, including contextual characteristic analysis, pos-tag analysis, and question-type analysis built on deep learning network architecture. Weights of extracted words through question analysis steps are combined with the best matching 25 (BM25) algorithm to find the best relevant paragraph of text and incorporated into the QA model to find the best and least noisy answer. The dataset for the question analysis step consists of 19,339 labeled questions covering a variety of topics. Results of the question analysis model are combined to train the question-answering model on the data set related to the learning regulations of Industrial University of Ho Chi Minh City. It includes 17,405 pairs of questions and answers for the training set and 1,600 pairs for the test set, where the robustly optimized BERT pre-training approach (RoBERTa) model has an F1-score accuracy of 74%. The model has improved significantly. For long and complex questions, the mode has extracted weights and correctly provided answers based on the question’s contents.

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Section: Results and Analysismentioning

confidence: 99%

Apply deep learning to improve the question analysis model in the Vietnamese question answering system

Phuc

Nghiem²,

Minh³

et al. 2023

IJECE

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show abstract

“…The second module recognized the activities performed in each window using a CNN model. The calculations performed on the CNN parameters required 77.06 MB of memory [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

Human activity recognition method using joint deep learning and acceleration signal

Meqdad

Hussein²,

Husain

et al. 2023

IJ-AI

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Many studies have been conducted on human activity recognition (HAR) in the last decade. Accordingly, deep learning (DL) algorithms have been given more attention in terms of classification of human daily activities. Deep neural networks (DNNs) compute and extract complex features on voluminous data through some hidden layers that require large memory and powerful graphics processing units (GPUs). So, this study proposes a new joint learning (JL) approach to classify human activities using inertial sensors. To this end, a large complex donor model based on a convolutional neural network (CNN) is used to transfer knowledge to a smaller model based on CNN referred to as the acceptor model. The acceptor model can be deployed on mobile devices and low-power hardware due to decreased computing costs and memory consumption. The wireless sensor data mining (WISDM) dataset is used to test the proposed model. According to the experimental results, the HAR system based on the JL algorithm outperforms than other methods.

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Combinatorial Analysis of Deep Learning and Machine Learning Video Captioning Studies: A Systematic Literature Review

Kehkashan,

Alsaeedi,

Yafooz

et al. 2024

IEEE Access

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Recent improvements formulated in the area of video captioning have brought rapid revolutions in its methods and the performance of its models. Machine learning and deep learning techniques are both employed in this regard. However, there is a lack of tracing the latest studies and their remarkable results. Although several studies have been proposed employing the ML and DL algorithms in different other areas, there is no systematic review utilizing the video captioning task. This study aims to examine, evaluate, and synthesize the studies into a thorough Systematic Literature Review (SLR) that provides a general overview of the methods used for video captioning. We performed the SLR to determine the research problems under which machine learning models were preferred over the deep learning models and vice versa. We collected a total of 1,656 studies retrieved from four electronic databases; Scopus, WoS, IEEE Xplore, and ACM, based on our search string from which 162 published studies passed the selection criteria related to one primary and two secondary research questions after a systematic process. Moreover, insufficient data collection and inefficient comparison of results are common issues during the review process. We conclude that the 2D/3D CNN for video feature extraction and LSTM for caption generation, BLEU and CIDEr performance evaluation tools and MSVD dataset are most frequently employed for video captioning. Our study is the pioneer in comparing the implementation of ML and DL algorithms employing the video captioning area. Thus, our study will accelerate the critical assessment of the state-of-the-art in other research fields of video analysis and human-computer interaction.

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Video captioning in Vietnamese using deep learning

Cited by 3 publications

References 23 publications

Apply deep learning to improve the question analysis model in the Vietnamese question answering system

Apply deep learning to improve the question analysis model in the Vietnamese question answering system

Human activity recognition method using joint deep learning and acceleration signal

Combinatorial Analysis of Deep Learning and Machine Learning Video Captioning Studies: A Systematic Literature Review

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