Foundation vision-language models have enabled remarkable zero-shot
transferability of the pre-trained representations to a wide range of
downstream tasks. However, to solve a new task, zero-shot transfer still
necessitates human guidance to define visual categories that appear in the
data. Here, we show that fully unsupervised transfer emerges when searching for
the labeling of a dataset that induces maximal margin classifiers in
representation spaces of different foundation models. We present TURTLE, a
fully unsupervised method that effectively employs this guiding principle to
uncover the underlying labeling of a downstream dataset without any supervision
and task-specific representation learning. We evaluate TURTLE on a diverse
benchmark suite of 26 datasets and show that it achieves new state-of-the-art
unsupervised performance. Furthermore, TURTLE, although being fully
unsupervised, outperforms zero-shot transfer baselines on a wide range of
datasets. In particular, TURTLE matches the average performance of CLIP
zero-shot on 26 datasets by employing the same representation space, spanning a
wide range of architectures and model sizes. By guiding the search for the
underlying labeling using the representation spaces of two foundation models,
TURTLE surpasses zero-shot transfer and unsupervised prompt tuning baselines,
demonstrating the surprising power and effectiveness of unsupervised transfer.

基于不同基础模型的表示空间引导搜索，TURTLE 发现了下游数据集中的潜在标签而无需任何监督学习，并在 26 个数据集上取得了新的无监督性能的最新成果，超越了零样例传递和无监督提示调优基线的平均表现。

放下你的标签：无监督迁移

Let Go of Your Labels with Unsupervised Transfer

For languages with no annotated resources, unsupervised transfer of natural
language processing models such as named-entity recognition (NER) from
resource-rich languages would be an appealing capability. However, differences
in words and word order across languages make it a challenging problem. To
improve mapping of lexical items across languages, we propose a method that
finds translations based on bilingual word embeddings. To improve robustness to
word order differences, we propose to use self-attention, which allows for a
degree of flexibility with respect to word order. We demonstrate that these
methods achieve state-of-the-art or competitive NER performance on commonly
tested languages under a cross-lingual setting, with much lower resource
requirements than past approaches. We also evaluate the challenges of applying
these methods to Uyghur, a low-resource language.

提出了一种基于双语单词嵌入的翻译方法，以改善跨语言的 named-entity recognition 性能，并使用 self-attention 来提高鲁棒性。在对常见语言的测试中达到了最先进或具有竞争力的 NER 性能。

具有最少资源的神经跨语言命名实体识别

Neural Cross-Lingual Named Entity Recognition with Minimal Resources

Unsupervised transfer of object recognition models from synthetic to real
data is an important problem with many potential applications. The challenge is
how to "adapt" a model trained on simulated images so that it performs well on
real-world data without any additional supervision. Unfortunately, current
benchmarks for this problem are limited in size and task diversity. In this
paper, we present a new large-scale benchmark called Syn2Real, which consists
of a synthetic domain rendered from 3D object models and two real-image domains
containing the same object categories. We define three related tasks on this
benchmark: closed-set object classification, open-set object classification,
and object detection. Our evaluation of multiple state-of-the-art methods
reveals a large gap in adaptation performance between the easier closed-set
classification task and the more difficult open-set and detection tasks. We
conclude that developing adaptation methods that work well across all three
tasks presents a significant future challenge for syn2real domain transfer.

本文介绍了一个新的大型基准测试 Syn2Real，其中包括了一个从 3D 物体模型渲染出的合成域以及两个包含相同物体类别的真实图像域，分别定义了三个相关任务：封闭集物体分类、开放集物体分类和物体检测。评估多种最先进的方法揭示了现有方法在不同任务上存在的许多适应性问题。