USD: Uncertainty-Based One-Phase Learning to Enhance Pseudo-Label Reliability for Semi-Supervised Object Detection-Reference-Cited by-同舟云学术

USD: Uncertainty-Based One-Phase Learning to Enhance Pseudo-Label Reliability for Semi-Supervised Object Detection

Published:2024 Issue: Volume:26 Page:6336-6347
ISSN:1520-9210
Container-title:IEEE Transactions on Multimedia
language:
Short-container-title:IEEE Trans. Multimedia

Author:

Chun Dayoung¹^ORCID,Lee Seungil²,Kim Hyun²^ORCID

Affiliation:

1. Inter-University Semiconductor Research Center, Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea

2. Department of Electrical and Information Engineering, The Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul, South Korea

Funder

Institute of Information and Communications Technology Planning and Evaluation

Ministry of Science and ICT, South Korea

Development of Self-learnable Mobile Recursive Neural Network Processor Technology

Ministry of Science and ICT, Korea

Institute for Information and Communications Technology Planning and Evaluation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6046/10384483/10378738.pdf?arnumber=10378738

Reference56 articles.

1. Understanding how noise affects the accuracy of CNN image classification;Lau;J. Appl. Technol. Innov.,2021

2. How Does the Data set Affect CNN-based Image Classification Performance?

3. The Effect of Improving Annotation Quality on Object Detection Datasets: A Preliminary Study

4. Challenges with Label Quality for Supervised Learning

5. Adaptive Hypergraph Embedded Semi-Supervised Multi-Label Image Annotation

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