The Role of Attention Mechanism and Multi-Feature in Image Captioning

Dang, Tien X.; Na, In Seop; Kim, Soo-Hyung

doi:10.1145/3310986.3311002

Cited by 5 publications

(2 citation statements)

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“…From the overall hidden states of the recurrent layer, they derive variable specific hidden representations over time, which can be flexibility used for g-forecasting and temporal-variable level attentions. In his master's thesis, Lee [14] and Na et al [15,16] proposed a bidirectional Encoder-Decoder with dual-stage attention model that slightly modified a dual-stage attention-based recurrent neural network proposed by Qin and colleagues for multivariate time series prediction. In addition, he used the stock price transaction data of companies included in KODEX 200 to evaluate the performance of the proposed model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Cho

Kim

et al. 2021

Electronics

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Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO2 level.

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Section: Introductionmentioning

confidence: 99%

Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Cho

Kim

et al. 2021

Electronics

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“…For generating sentence descriptions for images, it is adapted to identify only image features relevant to generating words at each time step of the LSTM word generation sequence. Dang et al (2019) explored the importance of the attention mechanism in their work using two pretrained CNNs for multi-feature leaning. Comparing different architectures to test the effect of the attention mechanism, the results indicated that the attention mechanism improved performance significantly as the architecture with the attention layer performed better than the one without in terms of evaluation metrics.…”

Section: Attention Based Image Captioningmentioning

confidence: 99%

Bidirectional LSTM approach to image captioning with scene features

Agughalam

Pathak

Stynes

2021

Thirteenth International Conference on Digital Image Processing (ICDIP 2021)

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I hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project. ALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.

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