Video Retrieval System Using Parallel Multi-Class Recurrent Neural Network Based on Video Description

Khan, Gulraiz; Naveed, Humza; Khan, Zahid; Khan, Usman

doi:10.1109/icet.2018.8603598

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…GAN approaches are also popular, associated with learning-based hashing which was proposed for scalable image retrieval (Song et al 2018a). Video retrieval can be more challenging due to the requirement for understanding activities, interactions between objects and unknown context; RNNs have provided a natural extension that supports the extraction of sequential behaviour in this case (Jabeen et al 2018).…”

Section: Content Retrievalmentioning

confidence: 99%

Artificial intelligence in the creative industries: a review

2021

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This paper reviews the current state of the art in artificial intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically machine learning (ML) algorithms, is provided including convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs) and deep Reinforcement Learning (DRL). We categorize creative applications into five groups, related to how AI technologies are used: (i) content creation, (ii) information analysis, (iii) content enhancement and post production workflows, (iv) information extraction and enhancement, and (v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, ML-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of ML in domains with fewer constraints, where AI is the ‘creator’, remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human-centric—where it is designed to augment, rather than replace, human creativity.

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Section: Content Retrievalmentioning

confidence: 99%

Artificial intelligence in the creative industries: a review

2021

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“…Manchev and Spratling [79] proposed implemented a new training algorithm for recurrent networks, target propagation over time (TPTT), that outperforms traditional timebackpropagation (BPTT) on four of the research issues. On four simulated time lag tasks, the proposed algorithm is initially checked and compared to BPTT, and its output is also calculated using the sequential MNIST data collection.…”

Section: Related Workmentioning

confidence: 99%

The Prediction Process Based on Deep Recurrent Neural Networks: A Review

Zeebaree

Abdulazeez

Abdullrhman

et al. 2021

AJRCoS

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Prediction is vital in our daily lives, as it is used in various ways, such as learning, adapting, predicting, and classifying. The prediction of parameters capacity of RNNs is very high; it provides more accurate results than the conventional statistical methods for prediction. The impact of a hierarchy of recurrent neural networks on Predicting process is studied in this paper. A recurrent network takes the hidden state of the previous layer as input and generates as output the hidden state of the current layer. Some of deep Learning algorithms can be utilized in as prediction tools in video analysis, musical information retrieval and time series applications. Recurrent networks may process examples simultaneously, maintaining a state or memory that recreates an arbitrarily long background window. Long Short-Term Memory (LSTM) and Bidirectional RNN (BRNN) are examples of recurrent networks. This paper aims to give a comprehensive assessment of predictions based on RNN. Additionally, each paper presents all relevant facts, such as dataset, method, architecture, and the accuracy of the predictions they deliver.

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“…GAN approaches are also popular, associated with learning-based hashing which was proposed for scalable image retrieval in [71]. Video retrieval can be more challenging due to the requirement for understanding activities, interactions between objects and unknown context; RNNs have provided a natural extension that support the extraction of sequential behaviour in this case [72].…”

Section: Content Retrievalmentioning

confidence: 99%

Artificial Intelligence in the Creative Industries: A Review

Anantrasirichai,

Bull

2020

Preprint

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This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the 'creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -where it is designed to augment, rather than replace, human creativity.

show abstract

Video Retrieval System Using Parallel Multi-Class Recurrent Neural Network Based on Video Description

Cited by 6 publications

References 13 publications

Artificial intelligence in the creative industries: a review

Artificial intelligence in the creative industries: a review

The Prediction Process Based on Deep Recurrent Neural Networks: A Review

Artificial Intelligence in the Creative Industries: A Review

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