Two papers accepted to CVPR 2019

Two papers from our lab has been accepted to CVPR 2019!

  • Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations
    Jiwoon Ahn, Sunghyun Cho, Suha Kwak
    CVPR 2019 (oral presentation)
  • Deep Defocus Map Estimation using Domain Adaptation
    Junyong Lee, Sungkil Lee, Sunghyun Cho, Seungyong Lee
    CVPR 2019

Outstanding Student Paper Award at IPIU 2019

Jucheol Won was awarded Outstanding Paper Award Silver Award at IPIU 2019.

Janghun Jo was awarded Outstanding Paper Award Bronze Award at IPIU 2019.

Jae-Yong Park was awarded Outstanding Poster Paper Award at IPIU 2019.

Jiwoon Ahn was awarded Outstanding Poster Paper Award at IPIU 2019.


Deep Learning

The evolution of deep learning has been led by applications in computer vision, image processing, computer graphics, and computational photography such as object detection and recognition, image restoration, and image synthesis and manipulation. In our lab, we study deep learning such as new neural network architectures, and training strategies for improving the performance of such applications.

Blur map estimation. Left: input images with defocus and motion blur. Right: their corresponding blur map (red: defocus blur, blue: motion blur, black: no-blur) (Kim et al., Pacific Graphics 2018)

Neural network architecture for blur map estimation (Kim et al., Pacific Graphics 2018)


Single image super-resolution (left: low-resolution image upsampled by bicubic interpolation, right: single-image super-resolution result using a GAN based CNN) (Park et al., ECCV 2018)

Neural network arcthiecture for single image super-resolution (Park et al., ECCV 2018)