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Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection
Chaewon Park, Minhyeok Lee, Suhwan Cho +2
Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets. These approaches work by learning to mod…
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
Suhwan Cho, Minhyeok Lee, Seunghoon Lee +3
Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level. In unsupervised VOS, most state-of-the-art methods lever…
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection
Donghyeong Kim, Chaewon Park, Suhwan Cho +1
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some method…
Injecting 3D Perception of Controllable NeRF-GAN into StyleGAN for Editable Portrait Image Synthesis
Jeong-gi Kwak, Yuanming Li, Dongsik Yoon +3
Over the years, 2D GANs have achieved great successes in photorealistic portrait generation. However, they lack 3D understanding in the generation process, thus they suffer from mu…
Adverse Weather Image Translation with Asymmetric and Uncertainty-aware GAN
Jeong-gi Kwak, Youngsaeng Jin, Yuanming Li +3
Adverse weather image translation belongs to the unsupervised image-to-image (I2I) translation task which aims to transfer adverse condition domain (eg, rainy night) to standard do…