MILE combines incremental LoRA experts with prototype-guided gating to support continual semantic segmentation across domains and modalities while adding only a small number of parameters per task.
In: CVPR (2020)
2 Pith papers cite this work. Polarity classification is still indexing.
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A large pool of diverse artistic styles for style-transfer augmentation improves domain generalization in driving vision models more than repeated use of few styles or domain-matched styles, yielding the lightweight StyleMixDG method with gains on GTAV-to-real benchmarks.
citing papers explorer
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MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
MILE combines incremental LoRA experts with prototype-guided gating to support continual semantic segmentation across domains and modalities while adding only a small number of parameters per task.
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Evaluation of Randomization through Style Transfer for Enhanced Domain Generalization
A large pool of diverse artistic styles for style-transfer augmentation improves domain generalization in driving vision models more than repeated use of few styles or domain-matched styles, yielding the lightweight StyleMixDG method with gains on GTAV-to-real benchmarks.