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Domain Generalized Fashion Object Detection using Style Augmentation and Attention
http://doi.org/10.5626/JOK.2023.50.10.845
With the combination of fashion and computer vision, fashion object detection using deep learning has gained much interest. However, due to the nature of supervision, the performance of the model drops when images with different characteristics are used. We define the dataset with different characteristics and the characteristic of the domain as ‘domain’ and ‘style’, respectively, and propose a new augmentation method that mixes up the existing domain’s style to make a new style. We also use an attention method to extract important features from the images. Using a stylized fashion detection dataset, style deepfashion2, we show that the proposed method enhances performance within all domains.
VACS: Virtual Try-on Artifact Correction System using the Fashion Object Segmentation Method
Wonjung Park, Youjin Chung, Soonchan Park, Jinah Park
http://doi.org/10.5626/JOK.2022.49.10.802
Virtual try-on (VITON) technology is receiving a lot of attention with the development of Generative Adversarial Networks (GANs) [1]. Previous approaches to VITON synthesized 2D model images and in-shop clothing images using a generative model. However, when synthesizing the top, VITON erroneously changes pixels in unintended areas, such as the background and pants. In this study, we propose the VITON Artifact Correction System (VACS), which divides and protects targeted clothes synthesized in VITON by fashion object segmentation, and replaces the pixels corresponding to the remaining areas with the original model image to increase the realism of the final composition.
Interpolation Method for CT Image Reconstruction using Features Inferred by Self-Supervised Learning
http://doi.org/10.5626/JOK.2021.48.9.1007
Since volumetric data includes internal information, it has an advantage of performing quantitative analysis. Especially medical image data render 3D structures of internal organs, and cubic voxel is necessary for accurate visualization. However, CT image volume is acquired in low z-resolution to reduce X-ray dose exposure. Between slices, image interpolation is a necessary step for visualization as well as for 3D data analysis. In this paper, we propose a self-supervised learning algorithm as an interpolation method that uses the information from the high-resolution images to infer missing information between slices. To achieve this, downscaled slice images are given as the input of the network, and the network recovers the original slice images from the downscaled images. The result of our method outperformed the commonly practiced interpolation methods - nearest-neighbor and trilinear interpolation – in the field, with respect to estimating details. Also, we verified that the proposed algorithm performs comparably with the supervised model with the same network.
Real-time Simulation of Seas and Swells for Ship Maneuvering Simulators
Sekil Park, Jaeyong Oh, Jinah Park
Seas and swells are basic wave types in ocean surface simulation and are very important elements in the simulation of ocean background. In this paper, we propose a real-time simulation method, for reproducing realistic seas and swells, to be used in real-time simulators such as ship maneuvering simulators. Seas and swells have different visual properties. Swells have relatively longer wavelengths and round crests compared with seas, therefore they are visualized globally with large meshes and procedural methods. Parameters to illustrate swells are extracted from ocean wave spectra. Conversely, seas have shorter wavelengths and their characteristics are only clearly apparent near to the observation point. Here, we present visualization of seas based on a statistical wave model using ocean wave spectra, which provides realistic results in a reactively small area.
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