Search Results for author: Zhuoran Yu

Found 6 papers, 2 papers with code

EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning

no code implementations13 Jun 2022 Zhuoran Yu, Yin Li, Yong Jae Lee

However, it has been shown that softmax-based confidence scores in deep networks can be arbitrarily high for samples far from the training data, and thus, the pseudo-labels for even high-confidence unlabeled samples may still be unreliable.

Out-of-Distribution Detection

Group R-CNN for Weakly Semi-supervised Object Detection with Points

1 code implementation CVPR 2022 Shilong Zhang, Zhuoran Yu, Liyang Liu, Xinjiang Wang, Aojun Zhou, Kai Chen

The core of this task is to train a point-to-box regressor on well-labeled images that can be used to predict credible bounding boxes for each point annotation.

object-detection Object Detection +2

CrossMatch: Improving Semi-Supervised Object Detection via Multi-Scale Consistency

no code implementations29 Sep 2021 Zhuoran Yu, Yen-Cheng Liu, Chih-Yao Ma, Zsolt Kira

Inspired by the fact that teacher/student pseudo-labeling approaches result in a weak and sparse gradient signal due to the difficulty of confidence-thresholding, CrossMatch leverages \textit{multi-scale feature extraction} in object detection.

object-detection Object Detection +1

Scale-Equalizing Pyramid Convolution for Object Detection

2 code implementations CVPR 2020 Xinjiang Wang, Shilong Zhang, Zhuoran Yu, Litong Feng, Wayne Zhang

Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution.

Ranked #67 on Object Detection on COCO test-dev (using extra training data)

object-detection Object Detection

Scale Calibrated Training: Improving Generalization of Deep Networks via Scale-Specific Normalization

no code implementations31 Aug 2019 Zhuoran Yu, Aojun Zhou, Yukun Ma, Yudian Li, Xiaohan Zhang, Ping Luo

Experiment results show that SCT improves accuracy of single Resnet-50 on ImageNet by 1. 7% and 11. 5% accuracy when testing on image sizes of 224 and 128 respectively.

Data Augmentation Image Classification +1

Cannot find the paper you are looking for? You can Submit a new open access paper.