In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending. In specific, in the training process, the ID conditional DDPM is trained to generate face images with the desired identity. In the sampling process, we use the off-the-shelf facial expert models to make the model transfer source identity while preserving target attributes faithfully. During this process, to preserve the background of the target image and obtain the desired face swapping result, we additionally propose a target-preserving blending strategy. It helps our model to keep the attributes of the target face from noise while transferring the source facial identity. In addition, without any re-training, our model can flexibly apply additional facial guidance and adaptively control the ID-attributes trade-off to achieve the desired results. To the best of our knowledge, this is the first approach that applies the diffusion model in face swapping task. Compared with previous GAN-based approaches, by taking advantage of the diffusion model for the face swapping task, DiffFace achieves better benefits such as training stability, high fidelity, diversity of the samples, and controllability. Extensive experiments show that our DiffFace is comparable or superior to the state-of-the-art methods on several standard face swapping benchmarks.

我们提出了一种基于示例的图像翻译的新方法，称为匹配交织的扩散模型（MIDMS）。该任务的大多数现有方法都是基于GAN的匹配，然后代表了代代框架。但是，在此框架中，跨跨域的语义匹配难度引起的匹配误差，例如草图和照片，可以很容易地传播到生成步骤，从而导致结果退化。由于扩散模型的最新成功激发了克服GAN的缺点，我们结合了扩散模型以克服这些局限性。具体而言，我们制定了一个基于扩散的匹配和生成框架，该框架通过将中间扭曲馈入尖锐的过程并将其变形以生成翻译的图像，从而交织了潜在空间中的跨域匹配和扩散步骤。此外，为了提高扩散过程的可靠性，我们使用周期一致性设计了一种置信度的过程，以在翻译过程中仅考虑自信区域。实验结果表明，我们的MIDM比最新方法产生的图像更合理。

我们提出了一个新颖的建筑，以实现密集的对应关系。当前的最新方法是基于变压器的方法，它们专注于功能描述符或成本量集合。但是，尽管关节聚集会通过提供一个人（即图像的结构或语义信息）或像素匹配的相似性来提高一个或另一个，但并非两者都聚集，但并非两者都汇总，尽管关节聚集会相互促进。在这项工作中，我们提出了一个基于变压器的新型网络，该网络以利用其互补信息的方式交织了两种形式的聚合。具体而言，我们设计了一个自我发项层，该层利用描述符来消除嘈杂的成本量，并且还利用成本量以促进准确匹配的方式汇总特征。随后的跨意思层执行进一步的聚合，该聚集在图像的描述上，并由早期层的聚合输出有助于。我们通过层次处理进一步提高了性能，在该处理中，更粗糙的聚合指导那些处于优质水平的过程。我们评估了所提出的方法对密集匹配任务的有效性，并在所有主要基准上实现最先进的性能。还提供了广泛的消融研究来验证我们的设计选择。

本文介绍了一个新颖的成本聚合网络，称为变压器（VAT），称为体积聚集，以进行几次分割。变压器的使用可以通过在全球接收场上的自我注意来使相关图的聚集受益。但是，变压器处理的相关图的令牌化可能是有害的，因为令牌边界处的不连续性会降低令牌边缘附近可用的局部环境，并减少电感偏差。为了解决这个问题，我们提出了一个4D卷积的SWIN变压器，在该问题上，高维的SWIN变压器之前是一系列的小内核卷积，这些卷积将局部环境赋予所有像素并引入卷积归纳偏置。另外，我们通过在锥体结构中应用变压器来提高聚合性能，在锥体结构中，在更粗糙的水平上的聚集指导聚集在较好的水平上。然后，在查询的外观嵌入中，在随后的解码器中过滤变压器输出中的噪声。使用此模型，为所有标准基准设置了一个新的最新基准，以几次射击分段设置。结果表明，增值税还达到了语义通信的最先进的性能，而成本汇总也起着核心作用。

我们介绍一个新颖的成本聚合网络，用变压器（VAT）被复制体积聚集，通过使用卷曲和变压器来解决几次拍摄分段任务，以有效地处理查询和支持之间的高维相关映射。具体而言，我们提出了由卷嵌入模块组成的编码器，不仅将相关性图转换为更具易易概要，而且为成本聚合注入一些卷积电感偏置和体积变压器模块。我们的编码器具有金字塔形结构，让较粗糙的级别聚合来指导更精细的水平并强制执行互补匹配分数。然后，我们将输出送入我们的亲和感知解码器以及投影特征映射，以指导分割过程。组合这些组件，我们进行实验以证明所提出的方法的有效性，我们的方法为几次拍摄分割任务中的所有标准基准设置了新的最先进的。此外，我们发现所提出的方法甚至可以在语义对应任务中的标准基准中获得最先进的性能，尽管没有专门为此任务设计。我们还提供广泛的消融研究，以验证我们的建筑选择。培训的权重和代码可用于：https://seokju-cho.github.io/vat/。

我们提出了一种新的成本聚合网络，称为成本聚合变压器（CAT），在语义类似的图像之间找到密集的对应关系，其中具有大型类内外观和几何变化构成的额外挑战。成本聚合是匹配任务的一个非常重要的过程，匹配精度取决于其输出的质量。与寻址成本聚集的手工制作或基于CNN的方法相比，缺乏严重变形的鲁棒性或继承了由于接受领域有限而无法区分错误匹配的CNN的限制，猫探讨了初始相关图之间的全球共识一些建筑设计的帮助，使我们能够充分利用自我关注机制。具体地，我们包括外观亲和力建模，以帮助成本聚合过程，以消除嘈杂的初始相关映射并提出多级聚合，以有效地从分层特征表示中捕获不同的语义。然后，我们与交换自我关注技术和残留连接相结合，不仅要强制执行一致的匹配，而且还可以缓解学习过程，我们发现这些结果导致了表观性能提升。我们进行实验，以证明拟议模型在最新方法中的有效性，并提供广泛的消融研究。代码和培训的型号可以在https://github.com/sunghwanhong/cats提供。

Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning. However, the ICL performance does not scale well with the number of available training samples as it is limited by the inherent input length constraint of the underlying language model. Meanwhile, many studies have revealed that language models are also powerful feature extractors, allowing them to be utilized in a black-box manner and enabling the linear probing paradigm, where lightweight discriminators are trained on top of the pre-extracted input representations. This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. PALP inherits the scalability of linear probing and the capability of enforcing language models to derive more meaningful representations via tailoring input into a more conceivable form. Throughout in-depth investigations on various datasets, we verified that PALP significantly enhances the input representations closing the gap between ICL in the data-hungry scenario and fine-tuning in the data-abundant scenario with little training overhead, potentially making PALP a strong alternative in a black-box scenario.

Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i.e., slots) to fulfill a specific task. A series of approaches based on this framework achieved remarkable success on various TOD benchmarks. However, we argue that the current TOD benchmarks are limited to surrogate real-world scenarios and that the current TOD models are still a long way from unraveling the scenarios. In this position paper, we first identify current status and limitations of SF-TOD systems. After that, we explore the WebTOD framework, the alternative direction for building a scalable TOD system when a web/mobile interface is available. In WebTOD, the dialogue system learns how to understand the web/mobile interface that the human agent interacts with, powered by a large-scale language model.

There is significant interest in deploying machine learning algorithms for diagnostic radiology, as modern learning techniques have made it possible to detect abnormalities in medical images within minutes. While machine-assisted diagnoses cannot yet reliably replace human reviews of images by a radiologist, they could inform prioritization rules for determining the order by which to review patient cases so that patients with time-sensitive conditions could benefit from early intervention. We study this scenario by formulating it as a learning-augmented online scheduling problem. We are given information about each arriving patient's urgency level in advance, but these predictions are inevitably error-prone. In this formulation, we face the challenges of decision making under imperfect information, and of responding dynamically to prediction error as we observe better data in real-time. We propose a simple online policy and show that this policy is in fact the best possible in certain stylized settings. We also demonstrate that our policy achieves the two desiderata of online algorithms with predictions: consistency (performance improvement with prediction accuracy) and robustness (protection against the worst case). We complement our theoretical findings with empirical evaluations of the policy under settings that more accurately reflect clinical scenarios in the real world.

In this work, we explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics: stress tests with synthetic data. Basically, we design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. We examine a range of recently proposed evaluation metrics based on pretrained language models, for the tasks of open-ended generation, translation, and summarization. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics. For example, we find that BERTScore ignores truncation errors in summarization, and MAUVE (built on top of GPT-2) is insensitive to errors at the beginning of generations. Further, we investigate the reasons behind these blind spots and suggest practical workarounds for a more reliable evaluation of text generation.