120 citations · 255 across the 11 of their papers we have counts for
12 papers
FSPGD: Rethinking Black-box Attacks on Semantic Segmentation
Eun-Sol Park, MiSo Park, Yong-Goo Shin
Black-box adversarial attacks on semantic segmentation remain a challenging problem, particularly in the black-box transfer attack setting where perturbations crafted on a surrogat…
Rethinking Image Skip Connections in StyleGAN2
Seung Park, Yong-Goo Shin
Various models based on StyleGAN have gained significant traction in the field of image synthesis, attributed to their robust training stability and superior performances. Within t…
Improving GANs with a Feature Cycling Generator
Seung Park, Yong-Goo Shin
Generative adversarial networks (GANs), built with a generator and discriminator, significantly have advanced image generation. Typically, existing papers build their generators by…
Image Generation with Self Pixel-wise Normalization
Yoon-Jae Yeo, Min-Cheol Sagong, Seung Park +2
Region-adaptive normalization (RAN) methods have been widely used in the generative adversarial network (GAN)-based image-to-image translation technique. However, since these appro…
A Novel Generator with Auxiliary Branch for Improving GAN Performance
Seung Park, Yong-Goo Shin
The generator in the generative adversarial network (GAN) learns image generation in a coarse-to-fine manner in which earlier layers learn the overall structure of the image and th…
Generative Convolution Layer for Image Generation
Seung Park, Yong-Goo Shin
This paper introduces a novel convolution method, called generative convolution (GConv), which is simple yet effective for improving the generative adversarial network (GAN) perfor…
PConv: Simple yet Effective Convolutional Layer for Generative Adversarial Network
Seung Park, Yoon-Jae Yeo, Yong-Goo Shin
This paper presents a novel convolutional layer, called perturbed convolution (PConv), which focuses on achieving two goals simultaneously: improving the generative adversarial net…
Simple yet Effective Way for Improving the Performance of GAN
Yong-Goo Shin, Yoon-Jae Yeo, Sung-Jea Ko
In adversarial learning, discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or non-robust features.…
Fast and Accurate 3D Hand Pose Estimation via Recurrent Neural Network for Capturing Hand Articulations
Cheol-hwan Yoo, Seo-won Ji, Yong-goo Shin +2
3D hand pose estimation from a single depth image plays an important role in computer vision and human-computer interaction. Although recent hand pose estimation methods using conv…
cGANs with Conditional Convolution Layer
Min-Cheol Sagong, Yong-Goo Shin, Yoon-Jae Yeo +2
Conditional generative adversarial networks (cGANs) have been widely researched to generate class conditional images using a single generator. However, in the conventional cGANs te…
PEPSI++: Fast and Lightweight Network for Image Inpainting
Yong-Goo Shin, Min-Cheol Sagong, Yoon-Jae Yeo +2
Among the various generative adversarial network (GAN)-based image inpainting methods, a coarse-to-fine network with a contextual attention module (CAM) has shown remarkable perfor…
Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays
Yong-Goo Shin, Seung Park, Yoon-Jae Yeo +2
Various power-constrained contrast enhancement (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the power demands of the display w…