Medical image segmentation (MIS) is essential for supporting disease diagnosis and treatment effect assessment. Despite considerable advances in artificial intelligence (AI) for MIS, clinicians remain skeptical of its utility, maintaining low confidence in such black box systems, with this problem being exacerbated by low generalization for out-of-distribution (OOD) data. To move towards effective clinical utilization, we propose a foundation model named EvidenceCap, which makes the box transparent in a quantifiable way by uncertainty estimation. EvidenceCap not only makes AI visible in regions of uncertainty and OOD data, but also enhances the reliability, robustness, and computational efficiency of MIS. Uncertainty is modeled explicitly through subjective logic theory to gather strong evidence from features. We show the effectiveness of EvidenceCap in three segmentation datasets and apply it to the clinic. Our work sheds light on clinical safe applications and explainable AI, and can contribute towards trustworthiness in the medical domain.

基于粒子的系统提供了一种灵活而统一的方法，可以模拟具有复杂动力学的物理系统。大多数现有的基于粒子系统的数据驱动的模拟器采用图形神经网络（GNN）作为网络骨架，因为粒子及其相互作用可以由图节点和图形边缘自然表示。但是，虽然基于粒子的系统通常包含数百千个颗粒，但由于粒子相互作用的数量增加，粒子相互作用的显式建模不可避免地会导致显着的计算开销。因此，在本文中，我们提出了一种基于变压器的新型方法，称为具有隐式边缘（TIE）的变压器，以无边缘方式捕获粒子相互作用的丰富语义。领带的核心思想是将涉及涉及配对粒子相互作用的计算分散到每个颗粒更新中。这是通过调整自我发项式模块以类似于GNN中图表的更新公式来实现的。为了提高领带的概括能力，我们进一步修改了可学习的特定材料的抽象粒子，以将全球材料的语义与本地粒子语义分开。我们评估了不同复杂性和材料不同领域的模型。与现有的基于GNN的方法相比，没有铃铛和哨子，TIE可以在所有这些领域中实现卓越的性能和概括。代码和模型可在https://github.com/ftbabi/tie_eccv2022.git上找到。

多视图子空间聚类传统上专注于集成异构特征描述以捕获更高维度信息。一种流行的策略是从不同视图生成常见的子空间，然后应用基于图形的方法来处理群集。但是，这些方法的性能仍然受到两个限制，即多视图融合模式以及融合过程与聚类任务之间的连接。为了解决这些问题，我们通过细粒度图形学习提出了一种新的多视图子空间聚类框架，可以在不同视图之间讲述本地结构之间的一致性，并比以前的重量规则更精细地集成所有视图。与文献中的其他模型不同，引入了点级图正规化和频谱聚类的重新介绍，以执行图形融合并将共享集群结构一起学习在一起。在五个真实数据集上进行了广泛的实验，表明该框架对SOTA算法具有可比性。

经过验证的多模态融合是提高扬声器跟踪的准确性和稳健性的有效方法，尤其是在复杂的情景中。但是，如何结合异构信息并利用多模态信号的互补性仍然是一个具有挑战性的问题。在本文中，我们提出了一种使用音频和视觉方式的扬声器跟踪的新型多模态感知跟踪器（MPT）。具体地，首先构建基于空间全局相干字段（STGCF）的新型声学图以用于异构信号融合，其采用相机模型将音频线索映射到与视觉提示一致的定位空间。然后，引入了多模态感知关注网络以导出测量受噪声干扰的间歇音频和视频流的可靠性和有效性的感知权重。此外，提出了一种独特的跨模式自我监督学习方法，以通过利用不同方式之间的互补性和一致性来模拟音频和视觉观测的置信度。实验结果表明，该拟议的MPT分别在标准和封闭数据集上实现了98.6％和78.3％的跟踪准确性，其在不利条件下展示了其鲁棒性，并且优于目前最先进的方法。

在本文中，我们提出了一个大型详细的3D面部数据集，FACESCAPE和相应的基准，以评估单视图面部3D重建。通过对FACESCAPE数据进行训练，提出了一种新的算法来预测从单个图像输入的精心索引3D面模型。 FACESCAPE DataSet提供18,760个纹理的3D面，从938个科目捕获，每个纹理和每个特定表达式。 3D模型包含孔径级面部几何形状，也被处理为拓扑均匀化。这些精细的3D面部模型可以表示为用于详细几何的粗糙形状和位移图的3D可线模型。利用大规模和高精度的数据集，进一步提出了一种使用深神经网络学习特定于表达式动态细节的新颖算法。学习的关系是从单个图像输入的3D面预测系统的基础。与以前的方法不同，我们的预测3D模型在不同表达式下具有高度详细的几何形状。我们还使用FACESCAPE数据来生成野外和实验室内基准，以评估最近的单视面重建方法。报告并分析了相机姿势和焦距的尺寸，并提供了忠诚和综合评估，并揭示了新的挑战。前所未有的数据集，基准和代码已被释放到公众以进行研究目的。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

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.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.