Generative and self-supervised domain adaptation for one-stage object detection-Reference-Cited by-同舟云学术

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Generative and self-supervised domain adaptation for one-stage object detection

Published:2021-09 Issue: Volume:11 Page:100071
ISSN:2590-0056
Container-title:Array
language:en
Short-container-title:Array

Author:

Fujii Kazuma,Kawamoto Kazuhiko^ORCID

Funder

Japan Society for the Promotion of Science London

Publisher

Elsevier BV

Reference30 articles.

1. Rich feature hierarchies for accurate object detection and semantic segmentation;Girshick,2014

2. Fast r-cnn;Girshick,2015

3. Faster r-cnn: towards real-time object detection with region proposal networks;Ren,2015

4. You only look once: unified, real-time object detection;Redmon,2016

5. Ssd: single shot multibox detector;Liu,2016

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1. An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision;Applied Sciences;2023-11-29

2. Salient object contour extraction based on pixel scales and hierarchical convolutional network;Array;2023-03

3. LFR-Net: Local feature residual network for single image dehazing;Array;2023-03

4. A multi camera unsupervised domain adaptation pipeline for object detection in cultural sites through adversarial learning and self-training;Computer Vision and Image Understanding;2022-09

5. A Semi-Supervised Learning Approach for Automatic Detection and Fashion Product Category Prediction with Small Training Dataset Using FC-YOLOv4;Applied Sciences;2022-08-12

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