Cyclic Self-Training With Proposal Weight Modulation for Cross-Supervised Object Detection
Author:
Affiliation:
1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China
2. Wormpex AI Research, Beijing, China
3. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
Funder
Fundamental Research Funds for the Central Universities
Australian Research Council Discovery Project
Australian Research Council Discovery Early Career Researcher Award
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Graphics and Computer-Aided Design,Software
Link
http://xplorestaging.ieee.org/ielx7/83/9991910/10087266.pdf?arnumber=10087266
Reference68 articles.
1. EHSOD: CAM-Guided End-to-End Hybrid-Supervised Object Detection with Cascade Refinement
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3. UFO2: A unified framework towards omni-supervised object detection;ren;Proc Eur Conf Comput Vis (ECCV),2020
4. Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
5. Low Shot Box Correction for Weakly Supervised Object Detection
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