我们旨在定量衡量医学图像分割模型的实际可用性：可以使用/信任模型的预测在多大程度上，多久和在哪些样品上进行样本。我们首先提出了一个度量，正确的信心等级相关性（CCRC），以捕获预测的置信度估计如何与其正确性分数相关。具有高价值CCRC的模型意味着其预测信心可靠地表明，哪些样本的预测更可能是正确的。由于CCRC没有捕获实际的预测正确性，因此仅仅指示预测模型是否既准确又可靠地用于实践中。因此，我们进一步提出了另一种可用区域估计（URE）的方法，同时量化了预测在一个估计中的置信度评估的正确性和可靠性。 URE提供了有关模型的预测在多大程度上可用的具体信息。此外，可以利用可用区域（UR）的大小来比较模型：具有较大UR的模型可以作为更可用的模型，因此可以将其视为更好的模型。六个数据集的实验验证了所提出的评估方法表现良好，为医学图像分割模型的实际可用性提供了具体和简洁的措施。代码可在https://github.com/yizhezhang2000/ure上提供。

开放式视频对象检测（OVD）旨在扩展词汇大小，以检测训练词汇以外的新颖类别的对象。最近的工作诉诸于预先训练的视觉模型中的丰富知识。但是，现有方法在提案级视觉语言对准方面无效。同时，这些模型通常遭受对基本类别的信心偏见，并且在新颖的类别上表现较差。为了克服挑战，我们提出了Medet，这是一个新颖有效的OVD框架，并具有建议挖掘和预测均衡。首先，我们设计了一个在线建议挖掘，以完善从粗到细的继承的视觉语义知识，从而允许提案级别以检测为导向的特征对齐。其次，基于因果推论理论，我们引入了班级的后门调整，以加强对新类别的预测，以提高整体OVD性能。对可可和LVIS基准的广泛实验验证了MEDET在检测新型类别的对象（例如可可的32.6％AP50）和LVI上的22.4％蒙版图中的优越性。

视觉变压器（VIT）正在改变对象检测方法的景观。 VIT的自然使用方法是用基于变压器的骨干替换基于CNN的骨干，该主链很简单有效，其价格为推理带来了可观的计算负担。更微妙的用法是DEDR家族，它消除了对物体检测中许多手工设计的组件的需求，但引入了一个解码器，要求超长时间进行融合。结果，基于变压器的对象检测不能在大规模应用中占上风。为了克服这些问题，我们提出了一种新型的无解码器基于完全变压器（DFFT）对象检测器，这是第一次在训练和推理阶段达到高效率。我们通过居中两个切入点来简化反对检测到仅编码单级锚点的密集预测问题：1）消除训练感知的解码器，并利用两个强的编码器来保留单层特征映射预测的准确性； 2）探索具有有限的计算资源的检测任务的低级语义特征。特别是，我们设计了一种新型的轻巧的面向检测的变压器主链，该主链有效地捕获了基于良好的消融研究的丰富语义的低级特征。 MS Coco基准测试的广泛实验表明，DFFT_SMALL的表现优于2.5％AP，计算成本降低28％，$ 10 \ $ 10 \乘以$ 10 \乘以$较少的培训时期。与尖端的基于锚的探测器视网膜相比，DFFT_SMALL获得了超过5.5％的AP增益，同时降低了70％的计算成本。

本文提出了一种任何时间的超分辨率方法（ARM），以解决过度参数化的单图像超分辨率（SISR）模型。我们的手臂是由三个观察结果激励的：（1）不同图像贴片的性能随不同大小的SISR网络而变化。 （2）计算开销与重建图像的性能之间存在权衡。 （3）给定输入图像，其边缘信息可以是估计其PSNR的有效选择。随后，我们训练包含不同尺寸的SISR子网的手臂超网，以处理各种复杂性的图像斑块。为此，我们构建了一个边缘到PSNR查找表，该表将图像补丁的边缘分数映射到每个子网的PSNR性能，以及子网的一组计算成本。在推论中，图像贴片单独分配给不同的子网，以获得更好的计算绩效折衷。此外，每个SISR子网都共享手臂超网的权重，因此不引入额外的参数。多个子网的设置可以很好地使SISR模型的计算成本适应动态可用的硬件资源，从而可以随时使用SISR任务。对不同大小的分辨率数据集的广泛实验和流行的SISR网络作为骨架验证了我们的手臂的有效性和多功能性。源代码可在https://github.com/chenbong/arm-net上找到。

最先进的命名实体识别（NER）模型在很大程度上依赖于完全注释的培训数据。但是，AC可访问的数据通常是不完全注释的，注释者通常缺乏目标域中的全面知识。通常，默认情况下，未注释的代币被认为是非实体，而我们强调这些令牌可能是任何实体的非实体。在这里，我们使用不完整的带注释数据研究NER mod-Eling，其中只有一部分命名实体是la-bel的，并且未标记的令牌被每个可能的标签都刻有多标签。路径可以分散训练模型从金路径（地面真相标签序列）中分散注意力，从而阻碍了学习能力。在本文中，我们提出了称为自适应顶级助攻的Adak-ner，该模型集中在一个较小的可行重新上，其中黄金路径更有可能被宠爱。我们通过广泛的英语和中文数据集证明了UR方法的优势，平均在2003年的F-评分中可以提高2％的速度，而在两个中文数据集中则超过10％，与先前的最新作品相比。

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.