no code implementations • 22 Jan 2024 • Woonghyun Ka, Jae Young Lee, Jaehyun Choi, Junmo Kim
In stereo-matching knowledge distillation methods of the self-supervised monocular depth estimation, the stereo-matching network's knowledge is distilled into a monocular depth network through pseudo-depth maps.
no code implementations • 22 Jan 2024 • Jae Young Lee, Woonghyun Ka, Jaehyun Choi, Junmo Kim
We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learning-based approaches, especially in safety-critical systems.
no code implementations • 4 Dec 2023 • Jae Young Lee, Wonjun Lee, Jaehyun Choi, Yongkwi Lee, Young Seog Yoon
Anomaly detection is a critical and challenging task that aims to identify data points deviating from normal patterns and distributions within a dataset.
1 code implementation • 20 Jul 2023 • Anita Rau, Sophia Bano, Yueming Jin, Pablo Azagra, Javier Morlana, Edward Sanderson, Bogdan J. Matuszewski, Jae Young Lee, Dong-Jae Lee, Erez Posner, Netanel Frank, Varshini Elangovan, Sista Raviteja, Zhengwen Li, Jiquan Liu, Seenivasan Lalithkumar, Mobarakol Islam, Hongliang Ren, José M. M. Montiel, Danail Stoyanov
We show that depth prediction in virtual colonoscopy is robustly solvable, while pose estimation remains an open research question.
no code implementations • 9 Jun 2023 • Dong-Jae Lee, Jae Young Lee, Hyounguk Shon, Eojindl Yi, Yeong-Hun Park, Sung-Sik Cho, Junmo Kim
While most lightweight monocular depth estimation methods have been developed using convolution neural networks, the Transformer has been gradually utilized in monocular depth estimation recently.
1 code implementation • CVPR 2023 • Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi, Jaejun Yoo, Junmo Kim
This allows our method to smoothly control the degree to which it preserves source features while generating images from an entirely new domain using only a single model.
no code implementations • 16 Jan 2023 • Jiwan Hur, Jae Young Lee, Jaehyun Choi, Junmo Kim
To apply LF-DeOcc in both LF datasets, we propose a framework, ISTY, which is defined and divided into three roles: (1) extract LF features, (2) define the occlusion, and (3) inpaint occluded regions.
1 code implementation • 29 Apr 2022 • Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi, Junmo Kim
Owing to the disentangled feature space, our method can smoothly control the degree of the source features in a single model.
no code implementations • 29 Sep 2021 • Sewhan Chun, Jae Young Lee, Junmo Kim
The policy search method with the best level of input data dependency involves training a loss predictor network to estimate suitable transformations for each of the given input image in independent manner, resulting in instance-level transformation extraction.