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.
no code implementations • 2 Nov 2023 • Jiwan Hur, Jaehyun Choi, Gyojin Han, Dong-Jae Lee, Junmo Kim
Training diffusion models on limited datasets poses challenges in terms of limited generation capacity and expressiveness, leading to unsatisfactory results in various downstream tasks utilizing pretrained diffusion models, such as domain translation and text-guided image manipulation.
no code implementations • 2 Jul 2023 • Gyojin Han, Dong-Jae Lee, Jiwan Hur, Jaehyun Choi, Junmo Kim
The proposed framework employs INRs to represent the secret data, which can handle data of various modalities and resolutions.
1 code implementation • CVPR 2023 • Gyojin Han, Jaehyun Choi, Haeil Lee, Junmo Kim
Model inversion attacks are a type of privacy attack that reconstructs private data used to train a machine learning model, solely by accessing the model.
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.
no code implementations • 29 Nov 2022 • Gyojin Han, Jaehyun Choi, Hyeong Gwon Hong, Junmo Kim
Training data generated by the proposed attack causes performance degradation on a specific task targeted by the attacker.
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.