13 citations · 30 across the 7 of their papers we have counts for
6 papers · 1 filter
PrintAnything: Learning an Intermediate Representation for 3D printing G-code Generation
Sangmin Hong, Daniel Sungho Jung, Heewon Kim +1
Point clouds are one of the most fundamental and widely used 3D representations, serving as the most basic geometric representation of 3D shapes. Nevertheless, most existing 3D pri…
HAZE-Net: High-Frequency Attentive Super-Resolved Gaze Estimation in Low-Resolution Face Images
Jun-Seok Yun, Youngju Na, Hee Hyeon Kim +2
Although gaze estimation methods have been developed with deep learning techniques, there has been no such approach as aim to attain accurate performance in low-resolution face ima…
Searching for Controllable Image Restoration Networks
Heewon Kim, Sungyong Baik, Myungsub Choi +2
Diverse user preferences over images have recently led to a great amount of interest in controlling the imagery effects for image restoration tasks. However, existing methods requi…
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results
Seungjun Nah, Sanghyun Son, Radu Timofte +1
Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challeng…
Fine-Grained Neural Architecture Search
Heewon Kim, Seokil Hong, Bohyung Han +2
We present an elegant framework of fine-grained neural architecture search (FGNAS), which allows to employ multiple heterogeneous operations within a single layer and can even gene…
Enhanced Deep Residual Networks for Single Image Super-Resolution
Bee Lim, Sanghyun Son, Heewon Kim +2
Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved p…