Global pooling is one of the most significant operations in many machine learning models and tasks, which works for information fusion and structured data (like sets and graphs) representation. However, without solid mathematical fundamentals, its practical implementations often depend on empirical mechanisms and thus lead to sub-optimal, even unsatisfactory performance. In this work, we develop a novel and generalized global pooling framework through the lens of optimal transport. The proposed framework is interpretable from the perspective of expectation-maximization. Essentially, it aims at learning an optimal transport across sample indices and feature dimensions, making the corresponding pooling operation maximize the conditional expectation of input data. We demonstrate that most existing pooling methods are equivalent to solving a regularized optimal transport (ROT) problem with different specializations, and more sophisticated pooling operations can be implemented by hierarchically solving multiple ROT problems. Making the parameters of the ROT problem learnable, we develop a family of regularized optimal transport pooling (ROTP) layers. We implement the ROTP layers as a new kind of deep implicit layer. Their model architectures correspond to different optimization algorithms. We test our ROTP layers in several representative set-level machine learning scenarios, including multi-instance learning (MIL), graph classification, graph set representation, and image classification. Experimental results show that applying our ROTP layers can reduce the difficulty of the design and selection of global pooling -- our ROTP layers may either imitate some existing global pooling methods or lead to some new pooling layers fitting data better. The code is available at \url{https://github.com/SDS-Lab/ROT-Pooling}.

在过去的十年中，AI AID毒品发现（AIDD）的计算方法和数据集策划的繁荣发展。但是，现实世界中的药物数据集经常表现出高度不平衡的分布，这在很大程度上被当前的文献忽略了，但可能会严重损害机器学习应用程序的公平性和概括。在这一观察结果的激励下，我们介绍了Imdrug，这是一个全面的基准标准，其开源python库由4个不平衡设置，11个AI-Ready数据集，54个学习任务和16种为不平衡学习量身定制的基线算法。它为涵盖广泛的药物发现管道（例如分子建模，药物靶标相互作用和逆合合成）的问题和解决方案提供了可访问且可定制的测试床。我们通过新的评估指标进行广泛的实证研究，以证明现有算法在数据不平衡情况下无法解决药物和药物挑战。我们认为，Imdrug为未来的研究和发展开辟了途径，在AIDD和深度不平衡学习的交集中对现实世界中的挑战开辟了道路。

分发比较在许多机器学习任务中起着核心作用，例如数据分类和生成建模。在这项研究中，我们提出了一种称为希尔伯特曲线投影（HCP）距离的新型度量，以测量具有高鲁棒性和低复杂性的两个概率分布之间的距离。特别是，我们首先使用希尔伯特曲线投射两个高维概率密度，以获得它们之间的耦合，然后根据耦合在原始空间中这两个密度之间的传输距离进行计算。我们表明，HCP距离是一个适当的度量标准，对于绝对连续的概率度量，定义明确。此外，我们证明，经验HCP距离在规律性条件下以不超过$ O（n^{ - 1/2d}）$的速度收敛到其人口。为了抑制差异性的诅咒，我们还使用（可学习的）子空间投影开发了HCP距离的两个变体。合成数据和现实世界数据的实验表明，我们的HCP距离是瓦斯汀距离的有效替代，其复杂性低并克服了切成薄片的瓦斯坦距离的缺点。

作为度量度量空间的有效度量，Gromov-Wasserstein（GW）距离显示了匹配结构化数据（例如点云和图形）问题的潜力。但是，由于其较高的计算复杂性，其实践中的应用受到限制。为了克服这一挑战，我们提出了一种新颖的重要性稀疏方法，称为SPAR-GW，以有效地近似GW距离。特别是，我们的方法没有考虑密集的耦合矩阵，而是利用一种简单但有效的采样策略来构建稀疏的耦合矩阵，并使用几个计算进行更新。我们证明了所提出的SPAR-GW方法适用于GW距离，并以任意地面成本适用于GW距离，并且将复杂性从$ \ Mathcal {o}（n^4）$降低到$ \ Mathcal {o}（n^{2） +\ delta}）$对于任意的小$ \ delta> 0 $。另外，该方法可以扩展到近似GW距离的变体，包括熵GW距离，融合的GW距离和不平衡的GW距离。实验表明，在合成和现实世界任务中，我们的SPAR-GW对最先进的方法的优越性。

全球合并是许多机器学习模型和任务中最重要的操作之一，但是在实践中，其实施通常是经验的。在这项研究中，我们通过最佳运输镜头开发了一个新颖而坚实的全球合并框架。我们证明，大多数现有的全球合并方法等同于解决不平衡最佳运输（UOT）问题的一些专业。使UOT问题的参数可学习，我们在同一框架中统一了各种全局合并方法，因此，为神经网络提出了一个称为UOT-Pooling（UOTP）的广义全局池层。除了基于经典的Sinkhorn尺度算法实现UOTP层外，我们设计了一种基于Bregman ADMM算法的新模型体系结构，该体系结构具有更好的数值稳定性，并且可以更有效地重现现有的池化层。我们在几种应用程序方案中测试了UOTP层，包括多构度学习，图形分类和图像分类。我们的UOTP层可以模仿常规的全球合并层，也可以学习一些新的合并机制，从而提高性能。

许多代表性图形神经网络，例如GPR-GNN和CHEBNET，具有曲线图谱滤波器的图形卷曲。但是，现有的工作要么应用预定义的滤波器权重，或者没有必要的约束来学习它们，这可能导致过度简化或不良滤波器。为了克服这些问题，我们提出了一种具有理论支持的新型图形神经网络的Bernnet，提供了一种简单但有效的设计和学习任意曲线图谱滤波器的方案。特别是，对于在图形的标准化Laplacian谱上的任何过滤器上，我们的Bernnet通过命令估计它是一个订单 - $ k $伯尔尼斯坦多项式近似，并通过设置伯尔尼斯坦的系数来设计其光谱特性。此外，我们可以基于观察的图形及其相关信号学习系数（和相应的滤波器权重），从而实现专门用于数据的BERNNET。我们的实验表明，Bernnet可以学习任意光谱滤波器，包括复杂的带抑制和梳状滤波器，并且它在真实的图形建模任务中实现了卓越的性能。代码可在https://github.com/ivam-he/bernnet上获得。

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.