no code implementations • 19 Mar 2024 • Junhao Cai, Yisheng He, Weihao Yuan, Siyu Zhu, Zilong Dong, Liefeng Bo, Qifeng Chen
Derived from OmniObject3D, OO3D-9D is the largest and most diverse dataset in the field of category-level object pose and size estimation.
1 code implementation • 4 Jul 2022 • Jun Cen, Peng Yun, Shiwei Zhang, Junhao Cai, Di Luan, Michael Yu Wang, Ming Liu, Mingqian Tang
Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e. g., autonomous driving, since it is closed-set and static.
no code implementations • 2 Dec 2021 • Jun Cen, Peng Yun, Junhao Cai, Michael Yu Wang, Ming Liu
The first step is solved by the finding that unknown objects are often classified as known objects with low confidence, and we show that the Euclidean distance sum based on metric learning is a better confidence score than the naive softmax probability to differentiate unknown objects from known objects.
1 code implementation • ICCV 2021 • Jun Cen, Peng Yun, Junhao Cai, Michael Yu Wang, Ming Liu
Incrementally learning these OOD objects with few annotations is an ideal way to enlarge the knowledge base of the deep learning models.
no code implementations • 18 Feb 2019 • Junhao Cai, Hui Cheng, Zhanpeng Zhang, Jingcheng Su
Although the model is trained using only RGB image, when changing the background textures, it also performs well and can achieve even 94% accuracy on the set of adversarial objects, which outperforms current state-of-the-art methods.