Collaborating Foundation Models for Domain Generalized Semantic Segmentation
arxiv(2023)
摘要
Domain Generalized Semantic Segmentation (DGSS) deals with training a model
on a labeled source domain with the aim of generalizing to unseen domains
during inference. Existing DGSS methods typically effectuate robust features by
means of Domain Randomization (DR). Such an approach is often limited as it can
only account for style diversification and not content. In this work, we take
an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative
FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In
detail, CLOUDS is a framework that integrates FMs of various kinds: (i) CLIP
backbone for its robust feature representation, (ii) generative models to
diversify the content, thereby covering various modes of the possible target
distribution, and (iii) Segment Anything Model (SAM) for iteratively refining
the predictions of the segmentation model. Extensive experiments show that our
CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under
varying weather conditions, notably outperforming prior methods by 5.6
6.7
https://github.com/yasserben/CLOUDS
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