no code implementations • 18 Mar 2024 • Mincheol Chang, Siyeong Lee, Jinkyu Kim, Namil Kim
Typical LiDAR-based 3D object detection models are trained in a supervised manner with real-world data collection, which is often imbalanced over classes (or long-tailed).
1 code implementation • 16 Nov 2020 • Kwanghee Choi, Siyeong Lee
Notable progress has been made in numerous fields of machine learning based on neural network-driven mutual information (MI) bounds.
no code implementations • 5 Oct 2020 • Kyunghun Kim, Yeohun Yun, Keon-Woo Kang, Kyeongbo Kong, Siyeong Lee, Suk-Ju Kang
The bidirectional boundary region rearrangement enables the generation of the missing region using bidirectional information similar to that of the image inpainting task, thereby generating the higher quality than the conventional methods using unidirectional information.
1 code implementation • 29 Jun 2020 • Jung Hee Kim, Siyeong Lee, Suk-Ju Kang
We tackle the problem in stack reconstruction-based methods by proposing a novel framework with a fully differentiable high dynamic range imaging (HDRI) process.
1 code implementation • ECCV 2018 • Siyeong Lee, Gwon Hwan An, Suk-Ju Kang
Because most images have a low dynamic range, recovering the lost dynamic range from a single low dynamic range image is still prevalent.
no code implementations • 19 Jan 2018 • Siyeong Lee, Gwon Hwan An, Suk-Ju Kang
The proposed model is based on a convolutional neural network composed of dilated convolutional layers, and infers LDR images with various exposures and illumination from a single LDR image of the same scene.