no code implementations • ECCV 2020 • Han-Ul Kim, Young Jun Koh, Chang-Su Kim
Especially, we propose a two-stage training scheme based on generative adversarial networks for unpaired learning.
1 code implementation • ECCV 2020 • Han-Ul Kim, Young Jun Koh, Chang-Su Kim
First, we represent various users' preferences for enhancement as feature vectors in an embedding space, called preference vectors.
no code implementations • ECCV 2018 • Minhyeok Heo, Jae-Han Lee, Kyung-Rae Kim, Han-Ul Kim, Chang-Su Kim
We propose a monocular depth estimation algorithm, which extracts a depth map from a single image, based on whole strip masking (WSM) and reliability-based refinement.
1 code implementation • ICCV 2017 • Jun-Tae Lee, Han-Ul Kim, Chul Lee, Chang-Su Kim
Then, we develop the line pooling layer to extract a feature vector for each candidate line from the feature maps.
Ranked #3 on Line Detection on SEL
no code implementations • ICCV 2015 • Han-Ul Kim, Dae-Youn Lee, Jae-Young Sim, Chang-Su Kim
The patch weights represent the importance of each patch in the description of foreground information, and are used to construct an object descriptor, called spatially ordered and weighted patch (SOWP) descriptor.