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Image style transfer is a method that can output styled images, which can both retain the original image content and add new artistic style. When using neural network, this method is referred as Neural Style Transfer (NST), which is a hot topic in the field of image processing and video processing. This article will provide a comprehensive overview...
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... result is a content aware generation algorithm, which provides meaningful control of the results, improves the quality of generated images by avoiding common faults, makes the results look more credible, and expands the functional scope of these algorithms. Refer to Figure 4. [12] proposed an approach consisting of a dual-stream deep convolution network as the loss network and edgepreserving filters as the style fusion model, with an additional similarity loss function added. ...
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Credit transfer is the process of transferring course credits of the subject to another programme or another institution to support and promote students’ flexibility in their study by reducing number of courses or subjects required to complete their studies. Currently, the enrolment of new degree students in Kolej Universiti Poly-Tech MARA Kuala Lu...
In this project, we will deepen our knowledge about style transfer by implementing one ourselves. First, we will have a look at the architecture and training process of the system. Afterwards, we will explain the loss functions that were used and talk about the datasets and preprocessing steps. We will then evaluate the results our system yields an...
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... Another recently-studied strategy to overcome training data limitations is RGB-to-IR cross-modal style transfer (CMST) [11], which is the focus of this work. The goal of CMST [5] is to transform RGB (i.e., color) imagery so that it appears as though it were collected under similar conditions using an IR camera [11]. ...
... Another recently-studied strategy to overcome training data limitations is RGB-to-IR cross-modal style transfer (CMST) [11], which is the focus of this work. The goal of CMST [5] is to transform RGB (i.e., color) imagery so that it appears as though it were collected under similar conditions using an IR camera [11]. Fig. 1 illustrates CMST with a real-world pair of co-collected RGB and IR imagery. ...
... Despite the aforementioned challenges of RGB-to-IR CMST, recently-proposed CMST methods have shown promise [11]. One general CMST strategy has been to handcraft models based upon known physics or heuristics relating RGB and IR imagery [12]. ...
Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However, publicly-available datasets that can be used for such training are limited in their size and diversity. To address this problem, we explore cross-modal style transfer (CMST) to leverage large and diverse color imagery datasets so that they can be used to train DNN-based IR image based object detectors. We evaluate six contemporary stylization methods on four publicly-available IR datasets - the first comparison of its kind - and find that CMST is highly effective for DNN-based detectors. Surprisingly, we find that existing data-driven methods are outperformed by a simple grayscale stylization (an average of the color channels). Our analysis reveals that existing data-driven methods are either too simplistic or introduce significant artifacts into the imagery. To overcome these limitations, we propose meta-learning style transfer (MLST), which learns a stylization by composing and tuning well-behaved analytic functions. We find that MLST leads to more complex stylizations without introducing significant image artifacts and achieves the best overall detector performance on our benchmark datasets.
... Transfer on Images and Videos 2018 [6] The paper presents a short survey of the current progress of NST from two aspects: the image optimization-based method and model-optimization-based method. It compares different types of the NST algorithms, applications of some proposals for future research. ...
Neural Style Transfer (NST) is a class of software algorithms that allows us to transform scenes, change/edit the environment of a media with the help of a Neural Network. NST finds use in image and video editing software allowing image stylization based on a general model, unlike traditional methods. This made NST a trending topic in the entertainment industry as professional editors/media producers create media faster and offer the general public recreational use. In this paper, the current progress in Neural Style Transfer with all related aspects such as still images and videos is presented critically. The authors looked at the different architectures used and compared their advantages and limitations. Multiple literature reviews focus on the Neural Style Transfer of images and cover Generative Adversarial Networks (GANs) that generate video. As per the authors’ knowledge, this is the only research article that looks at image and video style transfer, particularly mobile devices with high potential usage. This article also reviewed the challenges faced in applying for video neural style transfer in real-time on mobile devices and presents research gaps with future research directions. NST, a fascinating deep learning application, has considerable research and application potential in the coming years.
