现有域适应方法假设域差异是由一些离散属性和变化引起的很少的离散属性。因此，我们建议研究一个新问题，即通过连续变化的属性形成无限结构域的晶状体连续域适应（CDA）。利用两个标记的源域和几个观察到的未标记目标域数据的知识，CDA的目的是学习具有连续属性的整个数据分布的通用模型。除了提出新问题的贡献外，我们还提出了一种新颖的方法作为强大的CDA基线。具体而言，首先，我们提出了一种新颖的交替训练策略，以减少多个领域之间的差异，同时概括为看不见的目标域。其次，在估计跨域差异测量时，我们提出了连续性约束。最后，为了使差异与迷你批量大小相结合，我们设计了一个特定领域的队列，以维护源域的全局视图，从而进一步提高了适应性性能。事实证明，我们的方法可以使用广泛的实验实现CDA问题的最新问题。该代码可在https://github.com/spiresearch/cda上找到。

文档信息提取（DIE）由于其在现实世界中的各种高级应用而引起了越来越多的关注。尽管最近的文献已经取得了竞争成果，但在处理具有嘈杂的OCR结果或突变布局的复杂文档时，这些方法通常会失败。本文提出了用于现实世界情景的生成多模式网络（GMN），以解决这些问题，这是一种强大的多模式生成方法，没有预定义的标签类别。借助精心设计的空间编码器和模态感知的蒙版模块，GMN可以处理复杂的文档，这些文档很难序列化为顺序。此外，GMN可以容忍OCR结果中的错误，并且不需要字符级注释，这是至关重要的，因为对众多文档的细粒注释很费力，甚至需要具有专门域知识的注释者。广泛的实验表明，GMN在几个公共模具数据集上实现了新的最新性能，并超过了其他方法，尤其是在现实的场景中。

最近，在深图模型的帮助下，表结构识别取得了令人印象深刻的进展。其中大多数利用表格元素的单个视觉线索或通过早期融合来利用其他方式与其他方式结合起来，以推理其图形关系。然而，在多种模式方面既不是早期融合也不是单独的推理，可以适用于具有巨大多样性的表结构。相反，预计不同的方式将以不同的表案例的不同模式相互协作。在社区中，表层结构推理的跨性模特间交互的重要性仍未开发。在本文中，我们将其定义为异构表结构识别（异质-TSR）问题。旨在填补这种差距，我们提出了一种配备有堆叠的协作块的新型神经协作图机（NCGM），其替代地提取了模态上下文并以分层方式模拟了模范间交互。它可以代表表格元件的帧内模特关系更加强大，这显着提高了识别性能。我们还表明，所提出的NCGM可以调制在模态线索的背景下调节不同方式的不同方式的协同模式，这对于多元化表案例至关重要。基准测试的实验结果证明了我们所提出的NCGM实现最先进的性能，并通过较大的余量击败其他当代方法，特别是在挑战性的情况下。

随机特征方法已广泛用于大型机器学习中的内核近似。最近的一些研究已经探索了数据相关的功能，修改随机特征的随机oracle进行采样。虽然该领域的提出技术提高了近似值，但它们通常在单个学习任务上验证它们的适用性。在本文中，我们提出了一种特定于任务的评分规则，用于选择随机特征，该规则可以用于不同的应用程序具有一些调整。我们限制了我们对规范相关性分析（CCA）的注意，我们提供了一种新颖的，原则性指南，用于找到最大化规范相关性的得分函数。我们证明了这种方法，称为ORCCA，可以胜过（期望）具有默认内核的相应内核CCA。数值实验验证ORCCA明显优于CCA任务中的其他近似技术。

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.

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.