no code implementations • ECCV 2020 • Xuepeng Shi, Zhixiang Chen, Tae-Kyun Kim
Monocular 3D object detection plays an important role in autonomous driving and still remains challenging.
no code implementations • ICCV 2023 • Xuepeng Shi, Georgi Dikov, Gerhard Reitmayr, Tae-Kyun Kim, Mohsen Ghafoorian
Self-supervised monocular depth estimation (SSMDE) aims at predicting the dense depth maps of monocular images, by learning to minimize a photometric loss using spatially neighboring image pairs during training.
no code implementations • 6 Dec 2022 • Honggyu Choi, Zhixiang Chen, Xuepeng Shi, Tae-Kyun Kim
Unlike existing suboptimal methods, we propose a two-step pseudo-label filtering for the classification and regression heads in a teacher-student framework.
Ranked #14 on Semi-Supervised Object Detection on COCO 1% labeled data
1 code implementation • ICCV 2021 • Xuepeng Shi, Qi Ye, Xiaozhi Chen, Chuangrong Chen, Zhixiang Chen, Tae-Kyun Kim
The experimental results show that our method achieves the state-of-the-art performance on the monocular 3D Object Detection and Birds Eye View tasks of the KITTI dataset, and can generalize to images with different camera intrinsics.
Ranked #15 on Monocular 3D Object Detection on KITTI Cars Moderate
1 code implementation • CVPR 2018 • Xuepeng Shi, Shiguang Shan, Meina Kan, Shuzhe Wu, Xilin Chen
Rotation-invariant face detection, i. e. detecting faces with arbitrary rotation-in-plane (RIP) angles, is widely required in unconstrained applications but still remains as a challenging task, due to the large variations of face appearances.