We introduce LaGTran, a novel framework that utilizes readily available or
easily acquired text descriptions to guide robust transfer of discriminative
knowledge from labeled source to unlabeled target data with domain shifts.
While unsupervised adaptation methods have been established to address this
problem, they show limitations in handling challenging domain shifts due to
their exclusive operation within the pixel-space. Motivated by our observation
that semantically richer text modality has more favorable transfer properties,
we devise a transfer mechanism to use a source-trained text-classifier to
generate predictions on the target text descriptions, and utilize these
predictions as supervision for the corresponding images. Our approach driven by
language guidance is surprisingly easy and simple, yet significantly
outperforms all prior approaches on challenging datasets like GeoNet and
DomainNet, validating its extreme effectiveness. To further extend the scope of
our study beyond images, we introduce a new benchmark to study ego-exo transfer
in videos and find that our language-aided LaGTran yields significant gains in
this highly challenging and non-trivial transfer setting. Code, models, and
proposed datasets are publicly available at
this https URL

我们介绍了一种称为 LaGTran 的新框架，它利用现成或轻松获取的文本描述来指导具有领域偏移的标记源和未标记目标数据之间的强大的分类知识转移，并通过语言引导驱动的方法在各种具有挑战性的数据集上取得了显著优势。