Improved salient object detection using hybrid Convolution Recurrent Neural Network

Kousik, N.V.; Yuvaraj, N.; Raja, R. Arshath; Kalyanasundaram, Suresh; Patan, Rizwan; Gandomi, Amir H.

doi:10.1016/j.eswa.2020.114064

Cited by 66 publications

(19 citation statements)

References 14 publications

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“…The results are reported over the Microsoft COCO object detection dataset (Lin et al, 2014). Work in Karpathy and Fei-Fei (2015), Kousik et al (2021) integrated CNN and RNN in series, thus providing memorization capability for CNN in making a prediction. The results are reported for networks running on NVIDIA Jetson TX2, an embedded processor.…”

Section: Object Detectionmentioning

confidence: 99%

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

et al. 2022

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Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their decision. Unfortunately, the existing deep learning algorithms have a weak and nontransparent notion of memorization. Brain-inspired HyperDimensional Computing (HDC) is introduced as a model of human memory. Therefore, it mimics several important functionalities of the brain memory by operating with a vector that is computationally tractable and mathematically rigorous in describing human cognition. In this manuscript, we introduce a brain-inspired system that represents HDC memorization capability over a graph of relations. We propose GrapHD, hyperdimensional memorization that represents graph-based information in high-dimensional space. GrapHD defines an encoding method representing complex graph structure while supporting both weighted and unweighted graphs. Our encoder spreads the information of all nodes and edges across into a full holistic representation so that no component is more responsible for storing any piece of information than another. Then, GrapHD defines several important cognitive functionalities over the encoded memory graph. These operations include memory reconstruction, information retrieval, graph matching, and shortest path. Our extensive evaluation shows that GrapHD: (1) significantly enhances learning capability by giving the notion of short/long term memorization to learning algorithms, (2) enables cognitive computing and reasoning over memorization graph, and (3) enables holographic brain-like computation with substantial robustness to noise and failure.

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Section: Object Detectionmentioning

confidence: 99%

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

et al. 2022

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“…RNN addresses this issue with the help of a hidden layer. The most significant feature of RNN is the hidden state that remembers the sequence information [42]. RNN has a memory that remembers all the information about what has been calculated during the process.…”

Section: Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Latif

Driss

Boulila

et al. 2021

Sensors

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The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

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“…Wei et al [40] propose a fusion scheme of high land low features, which preserves rich details and background noise that have essential boundary information, thus producing accurate saliency maps. Kousik et al [41] designed a method by combining the idea of convolutional neural networks and recurrent neural networks (RNN) for video saliency detection. They created a convolutional recurrent neural network (CRNN), and the experiments reveal that the CRNN model achieves improved performance compared to other state-of-the-art saliency models.…”

Section: Related Workmentioning

confidence: 99%

Revise-Net: Exploiting Reverse Attention Mechanism for Salient Object Detection

Hussain

Karbhari²,

Ijaz

et al. 2021

Remote Sensing

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Recently, deep learning-based methods, especially utilizing fully convolutional neural networks, have shown extraordinary performance in salient object detection. Despite its success, the clean boundary detection of the saliency objects is still a challenging task. Most of the contemporary methods focus on exclusive edge detection modules in order to avoid noisy boundaries. In this work, we propose leveraging on the extraction of finer semantic features from multiple encoding layers and attentively re-utilize it in the generation of the final segmentation result. The proposed Revise-Net model is divided into three parts: (a) the prediction module, (b) a residual enhancement module, and (c) reverse attention modules. Firstly, we generate the coarse saliency map through the prediction modules, which are fine-tuned in the enhancement module. Finally, multiple reverse attention modules at varying scales are cascaded between the two networks to guide the prediction module by employing the intermediate segmentation maps generated at each downsampling level of the REM. Our method efficiently classifies the boundary pixels using a combination of binary cross-entropy, similarity index, and intersection over union losses at the pixel, patch, and map levels, thereby effectively segmenting the saliency objects in an image. In comparison with several state-of-the-art frameworks, our proposed Revise-Net model outperforms them with a significant margin on three publicly available datasets, DUTS-TE, ECSSD, and HKU-IS, both on regional and boundary estimation measures.

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Improved salient object detection using hybrid Convolution Recurrent Neural Network

Cited by 66 publications

References 14 publications

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions

Revise-Net: Exploiting Reverse Attention Mechanism for Salient Object Detection

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