12 citations · 24 across the 7 of their papers we have counts for
6 papers · 1 filter
ARNAI: Artifact Removal Network based on Autoencoding and Inpainting for Robust Spinal Image Segmentation and Measurement
Sang-Jin Park, Jinyoung Choi, Seokwon Kim +13
Purpose: This study aims to develop an AI framework applicable for postoperative imaging for automated measurement of spinopelvic parameters on radiographs with robustness to the p…
Holi-DETR: Holistic Fashion Item Detection Leveraging Contextual Information
Youngchae Kwon, Jinyoung Choi, Injung Kim
Fashion item detection is challenging due to the ambiguities introduced by the highly diverse appearances of fashion items and the similarities among item subcategories. To address…
Item Region-based Style Classification Network (IRSN): A Fashion Style Classifier Based on Domain Knowledge of Fashion Experts
Jinyoung Choi, Youngchae Kwon, Injung Kim
Fashion style classification is a challenging task because of the large visual variation within the same style and the existence of visually similar styles. Styles are expressed no…
Rethinking Garment Conditioning in Diffusion-based Virtual Try-On: Decouple, Don't Denoise
Kihyun Na, Jinyoung Choi, Injung Kim
Virtual Try-On (VTON) synthesizes realistic images of a person wearing a target garment, with broad applications in e-commerce and fashion. Diffusion-based dual-UNet methods achiev…
MF-LPR: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow
Kihyun Na, Junseok Oh, Youngkwan Cho +4
License plate recognition (LPR) is important for traffic law enforcement, crime investigation, and surveillance. However, license plate areas in dash cam images often suffer from l…
PECI-Net: Bolus segmentation from video fluoroscopic swallowing study images using preprocessing ensemble and cascaded inference
Dougho Park, Younghun Kim, Harim Kang +7
Bolus segmentation is crucial for the automated detection of swallowing disorders in videofluoroscopic swallowing studies (VFSS). However, it is difficult for the model to accurate…