1 code implementation • CVPR 2023 • Hyo-Jun Lee, HanUl Kim, Su-Min Choi, Seong-Gyun Jeong, Yeong Jun Koh
A novel monocular 3D pose and shape reconstruction algorithm, based on bi-contextual attention and attention-guided modeling (BAAM), is proposed in this work.
Ranked #1 on Autonomous Vehicles on ApolloCar3D
no code implementations • ICCV 2023 • Jongsung Lee, Gyeongsu Cho, Jeongin Park, Kyongjun Kim, Seongoh Lee, Jung-Hee Kim, Seong-Gyun Jeong, Kyungdon Joo
Based on the slanted MCI, we estimate a set of adaptive bins and a per-pixel probability map for depth estimation.
1 code implementation • NeurIPS 2022 • Jung-Hee Kim, Junwha Hur, Tien Phuoc Nguyen, Seong-Gyun Jeong
We present a self-supervised depth estimation approach using a unified volumetric feature fusion for surround-view images.
2 code implementations • CVPR 2022 • Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Heeyeon Kwon, Chang-Su Kim
Second, we generate a set of lane candidates by clustering the training lanes in the eigenlane space.
Ranked #28 on Lane Detection on TuSimple
1 code implementation • CVPR 2021 • Dongkwon Jin, Wonhui Park, Seong-Gyun Jeong, Chang-Su Kim
A novel algorithm to detect an optimal set of semantic lines is proposed in this work.
Ranked #1 on Line Detection on SEL
1 code implementation • ICCV 2019 • Seungmin Lee, Dongwan Kim, Namil Kim, Seong-Gyun Jeong
Recent works on domain adaptation exploit adversarial training to obtain domain-invariant feature representations from the joint learning of feature extractor and domain discriminator networks.
Ranked #16 on Domain Adaptation on VisDA2017
1 code implementation • ICCV 2019 • Serim Ryou, Seong-Gyun Jeong, Pietro Perona
We propose a novel loss function that dynamically rescales the cross entropy based on prediction difficulty regarding a sample.
1 code implementation • 26 Jun 2017 • Seong-Gyun Jeong, Jiwon Kim, Sujung Kim, Jaesik Min
We propose an image based end-to-end learning framework that helps lane-change decisions for human drivers and autonomous vehicles.
no code implementations • 8 Dec 2016 • Seong-Gyun Jeong, Yuliya Tarabalka, Nicolas Nisse, Josiane Zerubia
We propose a novel tree-like curvilinear structure reconstruction algorithm based on supervised learning and graph theory.