DRCO: Dense-Label Refinement and Cross Optimization for Semi-Supervised Object Detection
In semi-supervised object detection (SSOD), the methods based on dense pseudo-labeling bypass complex post-processing while maintaining competitive performance compared to the methods based on sparse pseudo-labeling. However, there are still relatively few researches focused on the dense pseudo-labe...
Saved in:
Main Authors: | Yunlong Qin, Yanjun Li, Feifan Ji, Yan Liu, Yu Wang, Ji Xiang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10818482/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Semi-supervised dynamic community detection based on non-negative matrix factorization
by: Zhen-chao CHANG, et al.
Published: (2016-02-01) -
A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems
by: Zijian Jiang, et al.
Published: (2025-01-01) -
SensorDBSCAN: Semi-Supervised Active Learning Powered Method for Anomaly Detection and Diagnosis
by: Petr Ivanov, et al.
Published: (2025-01-01) -
A Novel Approach to Test-Induced Defect Detection in Semiconductor Wafers, Using Graph-Based Semi-Supervised Learning (GSSL)
by: Pedram Aridas, et al.
Published: (2025-01-01) -
Study of implicit information semi-supervised learning algorithm
by: Guo-dong LIU, et al.
Published: (2015-10-01)