这项工作介绍了称为B-CANF的B帧编码框架，该框架利用有条件的增强标准化流量来进行B框架编码。学到的B框架编码的探索较少，更具挑战性。B-CANF是由有条件的P框架编码的最新进展的动机，是将基于流的模型应用于条件运动和框架间编码的首次尝试。B-CANF功能帧型自适应编码，该编码可以学习层次B框架编码更好的位分配。B-Canf还引入了一种特殊类型的B帧，称为B*-Frame，以模拟P框架编码。在常用数据集上，B-CANF达到了最新的压缩性能，在随机访问配置下显示了与HM-16.23相当的BD速率结果（在PSNR-RGB方面）。

在本文中，我们描述了一种数据驱动的方法，用于开发艾米丽（Emily），一种情绪感染的开放域聊天机器人。提出的数据增强方法可以从多转话对话中明确模拟阳性过渡（PT）情感数据。我们使用PT情感数据构建对话语料库，并将其发布供公众使用。通过使用生产的PT增强对话进行验证的对话模型，我们能够开发一种情感感染性的开放式聊天机器人，该聊天机器人在各种情绪影响度指标中表现出几乎人类的表现。我们对艾米丽（Emily）进行评估，以针对一些最先进的（SOTA）开放域聊天机器人，并显示拟议方法的有效性。

我们使用2-wasserstein空间的几何特性在一组概率度量之间发展了一个投影概念。它是为一般的多元概率度量而设计的，在计算上有效地实施，并在常规设置中提供了独特的解决方案。这个想法是使用广义的大地测量学处理瓦斯汀空间的常规切线锥。它的结构和计算属性使该方法适用于各种设置，从因果推断到对象数据的分析。估计因果效应的应用将合成控制的概念概括为具有个体级异质性的多元数据，以及一种在所有时间段内共同估算最佳权重的方法。

许多文献表明，基于及时的学习是使用大型预训练的语言模型的有效方法。最近的作品还展示了通过插入适当的提示来指导聊天机器人输出的可能性。基于梯度的方法通常用于扰动提示。但是，某些语言模型甚至无法为公众提供。在这项工作中，我们首先探讨了提示和加强学习（RL）与转向模型的生成的组合，而无需访问任何模型的参数。其次，为了减少培训工作并增强对看不见的任务的普遍性，我们应用多任务学习以使模型学会更好地对新任务进行推广。实验结果表明，我们提出的方法可以成功控制几个最新的（SOTA）对话模型，而无需访问其参数。此外，该模型证明了与基线模型更少的步骤快速适应看不见的任务的强大能力。

会话代理已成为简单任务允许情况的一般人群的组成部分。然而，这些系统尚未对各种和少数群体的任何社会影响，例如，帮助患有神经系统障碍的人，例如ALS和言语，语言和社交交流障碍的人。语言模型技术可以发挥巨大作用，以帮助这些用户进行日常沟通和社交互动。要启用此群体，我们构建了一个对话系统，可以使用CUES或关键字的用户控制。我们构建可以在用于控制响应生成的对话响应上下文中建立相关提示的模型，并可以加快通信。我们还介绍了一个关键字丢失来限制模型输出。我们在定性和定量上展示我们的模型可以有效地将关键字诱导到模型响应中，而不会降低响应的质量。在使用退行性障碍的人的使用情况的背景下，我们展示了对我们的提示或关键字预测器和可控对话系统的人类评估，并显示我们的模型比没有控制的模型更好地表现更好。我们的研究表明，在结束到结束响应生成模型的关键字控制是强大的，可以使用户能够与退行性疾病启用和赋予日常通信的日常沟通。

零拍分类问题的大多数现有算法通常依赖于类别之间基于属性的语义关系，以实现新型类别的分类而不观察其任何实例。但是，训练零拍分类模型仍然需要训练数据集中的每个类（甚至是实例）的属性标记，这也是昂贵的。为此，在本文中，我们提出了一个新的问题场景：“我们是否能够为新颖的属性探测器/分类器获得零射击学习，并使用它们自动注释数据集以进行标记效率？”基本上，仅给予一小组探测器，这些探测器都学会了识别一些手动注释的属性（即，所见属性），我们的目标是以零射学学习方式综合新颖属性的探测器。我们所提出的方法，零拍摄的属性（ZSLA），这是我们最好的知识中的第一个，通过应用SET操作首先将所看到的属性分解为基本属性，然后重新组合地解决这一新的研究问题。这些基本属性进入了新颖的属性。进行广泛的实验以验证我们合成探测器的能力，以便准确地捕获新颖性的语义，并与其他基线方法相比，在检测和定位方面表现出优越的性能。此外，在CALTECH-UCSD鸟类-200-2011 DataSet上使用仅32个属性，我们所提出的方法能够合成其他207个新颖的属性，而在由我们合成重新注释的数据集上培训的各种广义零拍分类算法属性探测器能够提供可比性的性能与手动地理注释有关的那些。

Unsupervised domain adaptation (UDA) for semantic segmentation is a promising task freeing people from heavy annotation work. However, domain discrepancies in low-level image statistics and high-level contexts compromise the segmentation performance over the target domain. A key idea to tackle this problem is to perform both image-level and feature-level adaptation jointly. Unfortunately, there is a lack of such unified approaches for UDA tasks in the existing literature. This paper proposes a novel UDA pipeline for semantic segmentation that unifies image-level and feature-level adaptation. Concretely, for image-level domain shifts, we propose a global photometric alignment module and a global texture alignment module that align images in the source and target domains in terms of image-level properties. For feature-level domain shifts, we perform global manifold alignment by projecting pixel features from both domains onto the feature manifold of the source domain; and we further regularize category centers in the source domain through a category-oriented triplet loss and perform target domain consistency regularization over augmented target domain images. Experimental results demonstrate that our pipeline significantly outperforms previous methods. In the commonly tested GTA5$\rightarrow$Cityscapes task, our proposed method using Deeplab V3+ as the backbone surpasses previous SOTA by 8%, achieving 58.2% in mIoU.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.

Panoptic Part Segmentation (PPS) unifies panoptic segmentation and part segmentation into one task. Previous works utilize separated approaches to handle thing, stuff, and part predictions without shared computation and task association. We aim to unify these tasks at the architectural level, designing the first end-to-end unified framework named Panoptic-PartFormer. Moreover, we find the previous metric PartPQ biases to PQ. To handle both issues, we make the following contributions: Firstly, we design a meta-architecture that decouples part feature and things/stuff feature, respectively. We model things, stuff, and parts as object queries and directly learn to optimize all three forms of prediction as a unified mask prediction and classification problem. We term our model as Panoptic-PartFormer. Secondly, we propose a new metric Part-Whole Quality (PWQ) to better measure such task from both pixel-region and part-whole perspectives. It can also decouple the error for part segmentation and panoptic segmentation. Thirdly, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross attention scheme to further boost part segmentation qualities. We design a new part-whole interaction method using masked cross attention. Finally, the extensive ablation studies and analysis demonstrate the effectiveness of both Panoptic-PartFormer and Panoptic-PartFormer++. Compared with previous Panoptic-PartFormer, our Panoptic-PartFormer++ achieves 2% PartPQ and 3% PWQ improvements on the Cityscapes PPS dataset and 5% PartPQ on the Pascal Context PPS dataset. On both datasets, Panoptic-PartFormer++ achieves new state-of-the-art results with a significant cost drop of 70% on GFlops and 50% on parameters. Our models can serve as a strong baseline and aid future research in PPS. Code will be available.