14 citations · 31 across the 12 of their papers we have counts for
19 papers
Deep Translation Prior: Test-time Training for Photorealistic Style Transfer
Sunwoo Kim, Soohyun Kim, Seungryong Kim
Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus havin…
MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation
Hojoon Lee, Dongyoon Hwang, Sunghwan Hong +3
Successful sequential recommendation systems rely on accurately capturing the user's short-term and long-term interest. Although Transformer-based models achieved state-of-the-art…
Learning Canonical 3D Object Representation for Fine-Grained Recognition
Sunghun Joung, Seungryong Kim, Minsu Kim +2
We propose a novel framework for fine-grained object recognition that learns to recover object variation in 3D space from a single image, trained on an image collection without usi…
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening
Sungha Choi, Sanghun Jung, Huiwon Yun +3
Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving. To addre…
On the confidence of stereo matching in a deep-learning era: a quantitative evaluation
Matteo Poggi, Seungryong Kim, Fabio Tosi +5
Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and rectified images. Along…
Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation
Minsu Kim, Sunghun Joung, Seungryong Kim +3
Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) deal with all the samples from the…