${R} {^{{2}}}$ of 0.9684, demonstrates leading performance in two typical cross-domain settings of data distribution scenarios, agriculture, and remote sensing (RS), exhibiting strong domain adaptation and generalization, surpassing advanced methods such as YOLOv8-UAV, PlantBiCNet, SLA, etc. We further studied the combination of regularization techniques, and feature re-mapping modules can effectively alleviate the domain invariance of the model. What is more, when the training set and validation set are set the same, the training performance of the model is better, but the premise is that there must be a proper data transformation strategy. This work provides a new perspective for understanding and solving the problem of domain difference in deep learning. The code and datasets can be accessed at https://github.com/Ye-Sk/TasselLFANetV2.">

TasselLFANetV2: Exploring Vision Models Adaptation in Cross-Domain (original) (raw)

IEEE Account

Purchase Details

Profile Information

Need Help?

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2026 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.