Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection

no code implementations • 26 Mar 2024 • Jiacheng Zhang, Jiaming Li, Xiangru Lin, Wei zhang, Xiao Tan, Junyu Han, Errui Ding, Jingdong Wang, Guanbin Li

Additionally, we present a DepthGradient Projection (DGP) module to mitigate optimization conflicts caused by noisy depth supervision of pseudo-labels, effectively decoupling the depth gradient and removing conflicting gradients.

Monocular 3D Object Detection object-detection +1

Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection

1 code implementation • ICCV 2023 • Jiaming Li, Xiangru Lin, Wei zhang, Xiao Tan, YingYing Li, Junyu Han, Errui Ding, Jingdong Wang, Guanbin Li

To tackle the confirmation bias from incorrect pseudo labels of minority classes, the class-rebalancing sampling module resamples unlabeled data following the guidance of the gradient-based reweighting module.

object-detection Object Detection +1

Single-pixel imaging based on deep learning

no code implementations • 25 Oct 2023 • Kai Song, Yaoxing Bian, Ku Wu, Hongrui Liu, Shuangping Han, Jiaming Li, Jiazhao Tian, Chengbin Qin, Jianyong Hu, Liantuan Xiao

Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors.

Super-Resolution

Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection

no code implementations • CVPR 2021 • Jiaming Li, Hongtao Xie, Jiahong Li, Zhongyuan Wang, Yongdong Zhang

Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries.