Segment scene in new domain by reduce bias when mix source and target domain with Guidance-Training
Segment scene in new domain by reduce bias when mix source and target domain with Guidance-Training
Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation
arXiv paper abstract https://arxiv.org/abs/2403.14995
arXiv PDF paper https://arxiv.org/pdf/2403.14995.pdf
Unsupervised Domain Adaptation (UDA) ... adjust models trained on a source domain to perform ... on a target domain without ... additional annotations ... domain adaptive semantic segmentation ... tackles UDA for dense prediction ... goal is to circumvent the need for ... annotations.
... propose a novel auxiliary task called Guidance Training.
This task facilitates the effective utilization of cross-domain mixed sampling techniques while mitigating distribution shifts from the real world.
... Guidance Training guides the model to extract and reconstruct the target-domain feature distribution from mixed data, followed by decoding the reconstructed target-domain features to make pseudo-label predictions.
Importantly, integrating Guidance Training incurs minimal training overhead and imposes no additional inference burden.
... demonstrate the efficacy of ... approach by integrating it with existing methods, consistently improving performance ...
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