activity
20192025
most citedPEPSI++: Fast and Lightweight Network for Image Inpainting

120 citations · 255 across the 11 of their papers we have counts for

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10 papers · 1 filter

cs.CV2025★ 1 cited

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…

cs.CV2024

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…

cs.CV2022

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…

cs.CV2022★ 11 cited

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…

cs.CV2021

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…

cs.CV2021★ 11 cited

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…

cs.CV2021★ 30 cited

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…

cs.CV2019★ 16 cited

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…

cs.CV2019★ 6 cited

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…

cs.CV2019★ 120 cited

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…