no code implementations • 22 Dec 2023 • Seungjun An, Seonghoon Park, Gyeongnyeon Kim, JeongYeol Baek, Byeongwon Lee, Seungryong Kim
With the increasing importance of video data in real-world applications, there is a rising need for efficient object detection methods that utilize temporal information.
1 code implementation • 21 Dec 2022 • Jongbeom Baek, Gyeongnyeon Kim, Seonghoon Park, Honggyu An, Matteo Poggi, Seungryong Kim
We propose MaskingDepth, a novel semi-supervised learning framework for monocular depth estimation to mitigate the reliance on large ground-truth depth quantities.
1 code implementation • 17 Dec 2022 • Gyeongnyeon Kim, Wooseok Jang, Gyuseong Lee, Susung Hong, Junyoung Seo, Seungryong Kim
Generative models have recently undergone significant advancement due to the diffusion models.
1 code implementation • CVPR 2022 • Soohyun Kim, Jongbeom Baek, JiHye Park, Gyeongnyeon Kim, Seungryong Kim
By augmenting such tokens with an instance-level feature extracted from the content feature with respect to bounding box information, our framework is capable of learning an interaction between object instances and the global image, thus boosting the instance-awareness.
no code implementations • 18 Mar 2022 • Jongbeom Baek, Gyeongnyeon Kim, Seungryong Kim
We propose a semi-supervised learning framework for monocular depth estimation.