The last mile of unsupervised domain adaptation (UDA) for semantic
segmentation is the challenge of solving the syn-to-real domain gap. Recent UDA
methods have progressed significantly, yet they often rely on strategies
customized for synthetic single-source datasets (e.g., GTA5), which limits
their generalisation to multi-source datasets. Conversely, synthetic
multi-source datasets hold promise for advancing the last mile of UDA but
remain underutilized in current research. Thus, we propose DEC, a flexible UDA
framework for multi-source datasets. Following a divide-and-conquer strategy,
DEC simplifies the task by categorizing semantic classes, training models for
each category, and fusing their outputs by an ensemble model trained
exclusively on synthetic datasets to obtain the final segmentation mask. DEC
can integrate with existing UDA methods, achieving state-of-the-art performance
on Cityscapes, BDD100K, and Mapillary Vistas, significantly narrowing the
syn-to-real domain gap.

提出了 DEC，这是一个灵活的多源数据集的 UDA 框架，通过将语义类别进行分类，并使用仅在合成数据集上训练的集成模型将它们的输出融合，来解决研究中的合成多源数据集的挑战，以获得最终的分割掩模，并可以与现有的 UDA 方法相结合，在 Cityscapes、BDD100K 和 Mapillary Vistas 上实现了最先进的性能。

划分、集成与征服：自主域适应中的最后一英里 - 车载语义分割

Divide, Ensemble and Conquer: The Last Mile on Unsupervised Domain  Adaptation for On-Board Semantic Segmentation

Knowledge-based, open-domain dialogue generation aims to build chit-chat
systems that talk to humans using mined support knowledge. Many types and
sources of knowledge have previously been shown to be useful as support
knowledge. Even in the era of large language models, response generation
grounded in knowledge retrieved from additional up-to-date sources remains a
practically important approach. While prior work using single-source knowledge
has shown a clear positive correlation between the performances of knowledge
selection and response generation, there are no existing multi-source datasets
for evaluating support knowledge retrieval. Further, prior work has assumed
that the knowledge sources available at test time are the same as during
training. This unrealistic assumption unnecessarily handicaps models, as new
knowledge sources can become available after a model is trained. In this paper,
we present a high-quality benchmark named multi-source Wizard of Wikipedia
(Ms.WoW) for evaluating multi-source dialogue knowledge selection and response
generation. Unlike existing datasets, it contains clean support knowledge,
grounded at the utterance level and partitioned into multiple knowledge
sources. We further propose a new challenge, dialogue knowledge plug-and-play,
which aims to test an already trained dialogue model on using new support
knowledge from previously unseen sources in a zero-shot fashion.

构建基于知识的开放领域对话生成技术旨在利用挖掘的知识与人类进行闲聊。本文提出了一个高质量的多源对话知识选择和生成评估基准，名为 multi-source Wizard of Wikipedia（Ms.WoW），用于评估多源对话知识选择和生成。与现有数据集不同的是，它包含了清晰的支持知识，以话语为单位分成多个知识来源。我们进一步提出了一个新挑战，即对话知识即插即用，旨在以零 - shot 的方式测试已经训练好的对话模型在使用未见过的新知识来源时的能力。

一个面向开放领域对话生成的知识即插即用测试平台

A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation

Human generation has achieved significant progress. Nonetheless, existing
methods still struggle to synthesize specific regions such as faces and hands.
We argue that the main reason is rooted in the training data. A holistic human
dataset inevitably has insufficient and low-resolution information on local
parts. Therefore, we propose to use multi-source datasets with various
resolution images to jointly learn a high-resolution human generative model.
However, multi-source data inherently a) contains different parts that do not
spatially align into a coherent human, and b) comes with different scales. To
tackle these challenges, we propose an end-to-end framework, UnitedHuman, that
empowers continuous GAN with the ability to effectively utilize multi-source
data for high-resolution human generation. Specifically, 1) we design a
Multi-Source Spatial Transformer that spatially aligns multi-source images to
full-body space with a human parametric model. 2) Next, a continuous GAN is
proposed with global-structural guidance and CutMix consistency. Patches from
different datasets are then sampled and transformed to supervise the training
of this scale-invariant generative model. Extensive experiments demonstrate
that our model jointly learned from multi-source data achieves superior quality
than those learned from a holistic dataset.

使用多源数据集和连续生成对抗网络设计了 UnitedHuman 框架，解决人体生成中的高分辨率问题，提高了生成图像的质量。