A survey of deep learning-based visual question answering

Huang, Tongyuan; Yang, Yibo; Yang, Xuejiao

doi:10.1007/s11771-021-4641-x

Cited by 5 publications

(6 citation statements)

References 77 publications

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“…As shown in Figure 3c, the jamming collision rate is guaranteed, but the transmitter will be silent for half of the timeslot length with low timeslot utilization. Obviously, this method cannot take into account both timeslot utilization and jamming collision rate [17]. Therefore, in order to reduce the jamming collision rate without reducing the timeslot utilization, SCQL is proposed based on the PQL algorithm in this paper, and its timeslot structure is shown in Figure 4.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Many anti-jamming algorithms have been proposed for various jamming styles, such as time-domain anti-random impulsive jamming proposed by Han et al [16], a PQL algorithm developed by Huang et al [17], and a QL serial structure proposed by Zhang et al [18]. Moreover, some studies have explored using deep reinforcement learning (DRL) for anti-jamming, such as Li et al's [19] cognitive radio network application and Wang et al's [20] intelligent transportation system case, as well as Yao et al's [21] QL-based anti-jamming strategy for UAV communication systems.…”

Section: Introductionmentioning

confidence: 99%

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Anti-Jamming Communication Using Slotted Cross Q Learning

Niu

Zhou

et al. 2023

Electronics

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Most of the existing intelligent anti-jamming communication algorithms model sensing, learning, and transmission as a serial process, and ideally assume that the duration of sensing and learning timeslots is very short, almost negligible. However, when the jamming environment changes rapidly, the sensing and learning time can no longer be ignored, and the adaptability of the wireless communication system to the time-varying jamming environment will be significantly reduced. To solve this problem, this paper proposes a parallel Q-learning (PQL) algorithm. In the case of long sensing and learning time, by modeling sensing, learning, and transmission as parallel processes, the time that the transmitter remains silent during sensing and learning is reduced. Aiming at the situation that the PQL algorithm is susceptible to jamming when the jamming changes faster, this paper proposes an intelligent anti-jamming algorithm for wireless communication based on Slot Cross Q-learning (SCQL). In the case of rapid change of jamming channel, the system can sense and learn the jamming patterns in multiple successive jamming periods at the same time in the same timeslot, and use multiple Q-tables to learn the jamming patterns in different jamming periods, so as to achieve the effect of reliable communication in the environment with rapid change of jamming. The simulation results show that the jamming collision rate of the proposed algorithm under the condition of intelligent blocking jamming is equivalent to that of the traditional Q-learning (QL), but the timeslot utilization rate is higher. Compared with PQL, the proposed algorithm has the same slot utilization and lower jamming collision rate. Compared with random frequency hopping (RFH) anti-jamming, the proposed algorithm not only has higher timeslot utilization, but also has lower jamming collision rate.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anti-Jamming Communication Using Slotted Cross Q Learning

Niu

Zhou

et al. 2023

Electronics

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“…Its core is to find a small amount of valuable information from a large amount of redundant information, that is, to make the more important information more easily noticed. Therefore, the essential problem of the attention mechanism is to give more weight to the valuable information [31]. Compared with other similar methods, the attention mechanism has the following characteristics: the structure can solve the most advanced multi-task model and does not use the loop model structure, and completely relies on the attention mechanism to establish the global dependence between the input and output.…”

Section: The Attention Mechanismmentioning

confidence: 99%

Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19

Cao

2022

Sustainability

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At present, machine learning has been successfully applied in many fields and has achieved amazing results. Meanwhile, over the past few years, the pandemic has transformed the transportation industry. The two hot issues prompt us to rethink the traditional problem of passenger flow forecasting. As a special structure embedded in the machine learning model, the attention mechanism is used to automatically learn and calculate the contribution degree of input data to output data. Therefore, this paper uses the attention mechanism to find the best model to predict OD passenger flow under COVID-19. Holiday characteristics, minimum temperature, COVID-19 factors, and past origin-destination (OD) passenger flow were used as input characteristics. In the first stage, the attention mechanism was used to capture the advantages of the trained random forest, extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), and Adaboost models, and then the MLP was trained. Afterward, the weight distribution of the two models is carried out by using the historical passenger flow. The multi-model attention+ MLP model was used to evaluate the OD passenger flow prediction of Dalian Metro Line 1 under COVID-19. All the possible choices in this process were taken as a comparison experiment. The results show that only the fusion model combining the attention mechanism of random forest and XGBoost with MLP has the highest prediction accuracy.

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“…With the huge data reserve, it is only necessary to annotate the required data and use neural networks to fit the data distribution to get good results. In the field of Natural Language Processing (NLP), textbased quiz systems input a given question and get the final answer by querying a structured or unstructured text [4][5]. These tasks require understanding both text and image modal quiz tasks, and in contrast to traditional text quiz, the queries include information about images in addition to text [6].…”

Section: Introductionmentioning

confidence: 99%

Research and implementation of visual question and answer system based on deep learning

2023

Applied Mathematics and Nonlinear Sciences

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With the development and improvement of deep learning technology, its application and practice in modal data (image, speech, and text) has been achieved tremendously. In this paper, based on the neural network modality class model in deep learning, we analyze its adaptation to the visual question and answer system, propose a visual question and answer model based on the gated attention mechanism, and construct a question, and answer prediction mechanism adapted to the recurrent neural network transfer model. To address the problem of low accuracy of the model on complex problems, the inference network module is built using visual inference so that the model can extract complex problem features to improve the inference capability of the model. By predicting answers through semantic information about text and visual elements in images, correlations across modalities, and inference, advances in natural language processing and computational vision have led to improved answer accuracy in deep learning-based visual quiz models. Multiple sets of experiments show that models with deep learning inference capabilities answer complex questions with significantly higher accuracy than other existing methods.

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A survey of deep learning-based visual question answering

Cited by 5 publications

References 77 publications

Anti-Jamming Communication Using Slotted Cross Q Learning

Anti-Jamming Communication Using Slotted Cross Q Learning

Multi-Model Attention Fusion Multilayer Perceptron Prediction Method for Subway OD Passenger Flow under COVID-19

Research and implementation of visual question and answer system based on deep learning

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