RETRACTED ARTICLE: Multi-edge optimized LSTM RNN for video summarization

Archana, N.; Malmurugan, N.

doi:10.1007/s12652-020-02025-8

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…then used a combination of Long Short-Term Memory (LSTM) and RNN networks to forecast the remaining effective life of the fuel cell. Due to its unique advantages, LSTM networks have also acquired giant success in other fields of temporal data processing, such as video analysis [33] and face recognition [34].…”

Section: Long Short-term Memorymentioning

confidence: 99%

Capsule Network Based on Double-layer Attention Mechanism and Multi-scale Feature Extraction for Remaining Life Prediction

Shang,

Feng,

et al. 2024

Neural Process Lett

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The era of big data provides a platform for high-precision RUL prediction, but the existing RUL prediction methods, which effectively extract key degradation information, remain a challenge. Existing methods ignore the influence of sensor and degradation moment variability, and instead assign weights to them equally, which affects the final prediction accuracy. In addition, convolutional networks lose key information due to downsampling operations and also suffer from the drawback of insufficient feature extraction capability. To address these issues, the two-layer attention mechanism and the Inception module are embedded in the capsule structure (mai-capsule model) for lifetime prediction. The first layer of the channel attention mechanism (CAM) evaluates the influence of various sensor information on the forecast; the second layer adds a time-step attention (TSAM) mechanism to the LSTM network to weigh the contribution of different moments of the engine's whole life cycle to the prediction, while weakening the influence of environmental noise on the prediction. The Inception module is introduced to perform multi-scale feature extraction on the weighted data to capture the degradation information to the maximum extent. Lastly, we are inspired to employ the capsule network to capture important position information of high and low-dimensional features, given its capacity to facilitate a more effective rendition of the overall features of the time-series data. The efficacy of the suggested model is assessed against other approaches and verified using the publicly accessible C-MPASS dataset. The end findings demonstrate the excellent prediction precision of the suggested approach.

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Section: Long Short-term Memorymentioning

confidence: 99%

Capsule Network Based on Double-layer Attention Mechanism and Multi-scale Feature Extraction for Remaining Life Prediction

Shang,

Feng,

et al. 2024

Neural Process Lett

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“…It is seen that colossal learning models that usage CNN and relative enhancements beat different models the degree that news and occasion-based video rundown. This can be seen from the study [6], wherein foreboding neural affiliations (RNNs) with multi-edge revived LSTM (MOLRVS) is utilized for oncewrapped up. The model adjusts the LSTM plan by adding different heaps of LSTM layers for better execution.…”

Section: Literature Review Deep Learning-based Video Summarizationmentioning

confidence: 99%

“…The model uses information like click rates, semantic relevance, textual-space to visual-space mapping and feature classification using CNNs in order to achieve classification accuracy of 81%, which makes the system deployable for real-time use cases. Another novel model that uses user-ranking for video segments in order to summarize the video is proposed in the studies [19][20][21]. This model uses a combination of 2D CNNs, 1D CNNs and LSTMs in order to generate a refined importance score for each video segment.…”

Section: Literature Review Deep Learning-based Video Summarizationmentioning

confidence: 99%

Gated Recurrent Units and Recurrent Neural Network Based Multimodal Approach for Automatic Video Summarization

Kaur¹,

Aljrees²,

Kumar³

et al. 2023

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A typical video record aggregation system requires the concurrent performance of a large number of image processing tasks, including but not limited to image acquisition, pre-processing, segmentation, feature extraction, verification, and description. These tasks must be executed with utmost precision to ensure smooth system performance. Among these tasks, feature extraction and selection are the most critical. Feature extraction involves converting the large-scale image data into smaller mathematical vectors, and this process requires great skill. Various feature extraction models are available, including wavelet, cosine, Fourier, histogram-based, and edge-based models. The key objective of any feature extraction model is to represent the image data with minimal attributes and no loss of information. In this study, we propose a novel feature-variance model that detects differences in video features and generates feature-reduced video frames. These frames are then fed into a GRU-based RNN model, which classifies them as either keyframes or non-keyframes. Keyframes are then extracted to create a summarized video, while non-keyframes are reduced. Various key-frame extraction models are also discussed in this section, followed by a detailed analysis of the proposed summarization model and its results. Finally, we present some interesting observations about the proposed model and suggest ways to improve it.

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“…Let the weight matrix of two adjacent fully connected layers be W ∈ m×n , and perform SVD decomposition of W , as in (19).…”

Section: Full Connection Layer Decomposition Based On Svdmentioning

confidence: 99%

“…Archana et al [19] used multi-edge optimization algorithm based on discrete wavelet transform to optimize LSTM and reduce the model processing time. Gokhan et al [20] used Leaky rectifying linear unit (Leaky ReLu) as the activation function to process the attenuation gradient of neurons.…”

Section: Introduction a Correlational Researchmentioning

confidence: 99%

Fast Video Summary Generation Based On Low Rank Tensor Decomposition

Sheng-tao

Liu

2021

IEEE Access

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This paper deals with the problem of large number of parameters and complex calculation in video abstract generation of Fully Connected Network and Convolutional Neural Network. At the same time, the training and testing of such model need a lot of time and computer resources. We came up with a deep learning network parameter compression method based on Singular Value Decomposition(SVD) and Trucker Decomposition (TD) is proposed to generate the video summaries. The experiment was compared with other methods on TVSum and SumMe dataset, and the F1 value was 55.3% in TVSum dataset and 46.8% in SumMe dataset. At the same time, the degree of test time shortening under the same data volume is taken as the evaluation basis. The experimental results show that the proposed method achieves 1.04 times of acceleration in the SVD forward calculation, and 1.29 times of acceleration in the TD forward calculation. In a conclusion, the neural network model based on low-rank decomposition can effectively save computer resources and the time consumed by running programs.

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RETRACTED ARTICLE: Multi-edge optimized LSTM RNN for video summarization

Cited by 9 publications

References 15 publications

Capsule Network Based on Double-layer Attention Mechanism and Multi-scale Feature Extraction for Remaining Life Prediction

Capsule Network Based on Double-layer Attention Mechanism and Multi-scale Feature Extraction for Remaining Life Prediction

Gated Recurrent Units and Recurrent Neural Network Based Multimodal Approach for Automatic Video Summarization

Fast Video Summary Generation Based On Low Rank Tensor Decomposition

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