Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon

Kim, Jong-Chan; Lim, Su-Chang; Choi, Jaehyeon

doi:10.3390/electronics11091303

Cited by 4 publications

(1 citation statement)

References 58 publications

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“…CNNs have been used to obtain technical features, being validated in various works as an emerging network with excellent capability in computer vision and pattern recognition, such as image and video classification and object recognition [24,25]. It is important to use features that can be obtained from each convolution layer in the CNN for VOT.…”

Section: Tracking Algorithm Based On Cnnmentioning

confidence: 99%

Siamese Trackers Based on Deep Features for Visual Tracking

Lim,

Huh,

Kim

2023

Electronics

Self Cite

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Visual object tracking poses challenges due to deformation of target object appearance, fast motion, brightness change, blocking due to obstacles, etc. In this paper, a Siamese network that is configured using a convolutional neural network is proposed to improve tracking accuracy and robustness. Object tracking accuracy is dependent on features that can well represent objects. Thus, we designed a convolutional neural network structure that can preserve feature information that is produced in the previous layer to extract spatial and semantic information. Features are extracted from the target object and search area using a Siamese network, and the extracted feature map is input into the region proposal network, where fast Fourier-transform convolution is applied. The feature map produces a probability score for the presence of an object region and an object in a region, where the similarities are high to search the target. The network was trained with a video dataset called ImageNet Large Scale Visual Recognition Challenge. In the experiment, quantitative and qualitative evaluations were conducted using the object-tracking benchmark dataset. The evaluation results indicated competitive results for some video attributes through various experiments. By conducting experiments, the proposed method achieved competitive results for some video attributes, with a success metric of 0.632 and a precision metric of 0.856 as quantitative values.

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Section: Tracking Algorithm Based On Cnnmentioning

confidence: 99%

Siamese Trackers Based on Deep Features for Visual Tracking

Lim,

Huh,

Kim

2023

Electronics

Self Cite

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How to train your pre-trained GAN models

Park,

Kim,

Park

et al. 2023

Appl Intell

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Generative Adversarial Networks (GAN) show excellent performance in various problems of computer vision, computer graphics, and machine learning, but require large amounts of data and huge computational resources. There is also the issue of unstable training. If the generator and discriminator diverge during the training process, the GAN is subsequently difficult to converge. In order to tackle these problems, various transfer learning methods have been introduced; however, mode collapse, which is a form of overfitting, often arises. Moreover, there were limitations in learning the distribution of the training data. In this paper, we provide a comprehensive review of the latest transfer learning methods as a solution to the problem, propose the most effective method of fixing some layers of the generator and discriminator, and discuss future prospects. The model to be used for the experiment is StyleGAN, and the performance evaluation uses Fréchet Inception Distance (FID), coverage, and density. Results of the experiment revealed that the proposed method did not overfit. The model was able to learn the distribution of the training data relatively well compared to the previously proposed methods. Moreover, it outperformed existing methods at the Stanford Cars, Stanford Dogs, Oxford Flower, Caltech-256, CUB-200–2011, and Insect-30 datasets.

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Deep Feature Based Siamese Network for Visual Object Tracking

Lim

Kim

2022

Energies

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One of the most important and challenging research subjects in computer vision is visual object tracking. The information obtained from the first frame consists of limited and insufficient information to represent an object. If prior information about robust representation that can represent an object well is not sufficient, object tracking fails when not robustly responding to changes in features of the target object according to various factors, namely shape, illumination variation, and scene distortion. In this paper, a real-time single object tracking algorithm is proposed based on a Siamese network to solve this problem. For the object feature extraction, we designed a fully convolutional neural network that removes a fully connected layer and configured a convolution block consisting of a bottleneck structure that preserves the information in a previous layer. This network was designed as a Siamese network, while a regional proposal network was combined at the end of the network for object tracking. The ImageNet Large-Scale Visual Recognition Challenge 2017 dataset was used to train the network in the pre-training phase. Then, in the experimental phase, the object tracking benchmark dataset was used to quantitatively evaluate the network. The experimental results revealed that the proposed tracking algorithm produced more competitive results compared to other tracking algorithms.

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Review for Examining the Oxidation Process of the Moon Using Generative Adversarial Networks: Focusing on Landscape of Moon

Cited by 4 publications

References 58 publications

Siamese Trackers Based on Deep Features for Visual Tracking

Siamese Trackers Based on Deep Features for Visual Tracking

How to train your pre-trained GAN models

Deep Feature Based Siamese Network for Visual Object Tracking

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