Cycle-Consistent Domain Adaptive Faster RCNN
Traditional object detection methods always assume both of the training and test data follow the same distribution, but this cannot always be guaranteed in the real world. Domain adaptive methods are proposed to handle this situation. However, existing methods generally ignore the semantic alignment...
Main Authors: | Dan Zhang, Jingjing Li, Lin Xiong, Lan Lin, Mao Ye, Shangming Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8822427/ |
Similar Items
-
Category-Level Adversaries for Semantic Domain Adaptation
by: Congcong Ruan, et al.
Published: (2019-01-01) -
Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
by: Xiaoqing Wang, et al.
Published: (2018-12-01) -
Pscenegan: Multi-Domain Particular Scenes Generation Based on Conditional Generative Adversarial Networks
by: Li-Li Jia, et al.
Published: (2019-01-01) -
Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks
by: Bilel Benjdira, et al.
Published: (2020-02-01) -
Double-Domain Imaging and Adaption for Person Re-Identification
by: Shuren Zhou, et al.
Published: (2019-01-01)