no code implementations • 6 Mar 2024 • Sungho Kang, YeongHyeon Park, Hyunkyu Park, Juneho Yi
To address the problem of scene depth estimation from oriental landscape painting images, we propose a novel framework that consists of two-step Image-to-Image translation method with CLIP-based image matching at the front end to predict the real scene image that best matches with the given oriental landscape painting image.
no code implementations • 6 Oct 2023 • YeongHyeon Park, Sungho Kang, Myung Jin Kim, Yeonho Lee, Hyeong Seok Kim, Juneho Yi
To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions.
Ranked #66 on Anomaly Detection on MVTec AD
Self-Supervised Anomaly Detection Supervised Anomaly Detection +1
no code implementations • 28 Aug 2023 • YeongHyeon Park, Sungho Kang, Myung Jin Kim, Hyeonho Jeong, Hyunkyu Park, Hyeong Seok Kim, Juneho Yi
In contrast, we note that containing of generalization ability in reconstruction can also be obtained simply from steep-shaped loss landscape.
no code implementations • 23 Aug 2021 • Seho Bae, Nizam Ud Din, Hyunkyu Park, Juneho Yi
The problem of photo-sketch matching is challenging because 1) there is large modality gap between photo and sketch, and 2) the number of paired training samples is insufficient to train deep learning based networks.