During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment. Against this metal artifact reduction (MAR) task, the existing deep-learning-based methods have gained promising reconstruction performance. Nevertheless, there is still some room for further improvement of MAR performance and generalization ability, since some important prior knowledge underlying this specific task has not been fully exploited. Hereby, in this paper, we carefully analyze the characteristics of metal artifacts and propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts, i.e., rotationally symmetrical streaking patterns. The proposed method rationally adopts Fourier-series-expansion-based filter parametrization in artifact modeling, which can better separate artifacts from anatomical tissues and boost the model generalizability. Comprehensive experiments executed on synthesized and clinical datasets show the superiority of our method in detail preservation beyond the current representative MAR methods. Code will be available at \url{https://github.com/hongwang01/OSCNet}

Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-training objectives are studied in existing solutions, the document textline, as an intrinsic granularity in VDU, has seldom been explored so far. A document textline usually contains words that are spatially and semantically correlated, which can be easily obtained from OCR engines. In this paper, we propose Wukong-Reader, trained with new pre-training objectives to leverage the structural knowledge nested in document textlines. We introduce textline-region contrastive learning to achieve fine-grained alignment between the visual regions and texts of document textlines. Furthermore, masked region modeling and textline-grid matching are also designed to enhance the visual and layout representations of textlines. Experiments show that our Wukong-Reader has superior performance on various VDU tasks such as information extraction. The fine-grained alignment over textlines also empowers Wukong-Reader with promising localization ability.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.

To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios.

了解动态场景中的3D运动对于许多视觉应用至关重要。最近的进步主要集中在估计人类等某些特定元素的活动上。在本文中，我们利用神经运动场来估计多视图设置中所有点的运动。由于颜色相似的点和与时变颜色的点的歧义，从动态场景中对动态场景进行建模运动是具有挑战性的。我们建议将估计运动的正规化为可预测。如果已知来自以前的帧的运动，那么在不久的将来的运动应该是可以预测的。因此，我们通过首先调节潜在嵌入的估计运动来引入可预测性正则化，然后通过采用预测网络来在嵌入式上执行可预测性。所提出的框架pref（可预测性正则化字段）比基于最先进的神经运动场的动态场景表示方法在PAR或更好的结果上取得了更好的成绩，同时不需要对场景的先验知识。

全面监督的人类网格恢复方法是渴望数据的，由于3D规定基准数据集的可用性有限和多样性，因此具有较差的概括性。使用合成数据驱动的训练范例，已经从合成配对的2D表示（例如2D关键点和分段掩码）和3D网格中训练了模型的最新进展，其中已使用合成数据驱动的训练范例和3D网格进行了训练。但是，由于合成训练数据和实际测试数据之间的域间隙很难解决2D密集表示，因此很少探索合成密集的对应图（即IUV）。为了减轻IUV上的这个领域差距，我们提出了使用可靠但稀疏表示的互补信息（2D关键点）提出的交叉代理对齐。具体而言，初始网格估计和两个2D表示之间的比对误差将转发为回归器，并在以下网格回归中动态校正。这种适应性的交叉代理对准明确地从偏差和捕获互补信息中学习：从稀疏的表示和浓郁的浓度中的稳健性。我们对多个标准基准数据集进行了广泛的实验，并展示了竞争结果，帮助减少在人类网格估计中生产最新模型所需的注释工作。

多模式实体对齐旨在确定两个不同的多模式知识图之间的等效实体，这些实体由与实体相关的结构三元组和图像组成。大多数先前的作品都集中在如何利用和编码不同模式中的信息，而由于模态异质性，因此在实体对齐中利用多模式知识并不是微不足道的。在本文中，我们提出了基于多模式对比度学习的实体比对模型McLea，以获得多模式实体对准的有效联合表示。与以前的工作不同，麦克莱尔（McLea）考虑了面向任务的模式，并为每个实体表示形式建模模式间关系。特别是，麦克莱（McLea）首先从多种模式中学习多个单独的表示，然后进行对比学习以共同对模式内和模式间相互作用进行建模。广泛的实验结果表明，在受监督和无监督的设置下，MCLEA在公共数据集上优于公共数据集的最先进的基线。

稀疏的一般矩阵乘法（SPGEMM）是许多科学应用中的基本构件。 SPGEMM的一项关键任务是计算或预测有效的内存分配和负载平衡的输出矩阵的结构（即，每个输出行的非零元素的数量），这会影响SPGEMM的整体性能。现有工作要么精确地计算出输出结构，要么采用基于上限或采样的方法来预测输出结构。但是，这些方法要么需要太多执行时间，要么不够准确。在本文中，我们提出了一种基于采样的新方法，与现有基于采样的方法相比，具有更好的精度和低成本。该方法首先通过利用中间产品的数量（表示为flop）和同一采样结果矩阵的非零元素（表示为NNZ）来预测SPGEMM的压缩比。然后，通过将每次输出行除以预测的压缩率来获得预测的输出结构。我们还建议使用优化的计算开销的基于采样的方法的参考设计，以证明所提出的方法的准确性。我们构建具有各种矩阵维度和稀疏结构的625个测试用例，以评估预测准确性。实验结果表明，在最坏的情况下，所提出方法和参考设计的绝对相对误差分别为1.56 \％和8.12 \％，分别为25 \％和156 \％。

基于点击的交互式图像分割的目的是获得用户交互有限的精确对象分割掩码，即通过最少数量的用户点击。现有方法要求用户提供所有点击：首先检查分割掩码，然后在迭代区域上提供标记区域错误的点。我们提出一个问题：我们的模型可以直接预测在哪里单击，以进一步降低用户交互成本？为此，我们提出{\ pseudoclick}，这是一个通用框架，使现有的分割网络能够提出下一步点击。这些自动生成的点击，称为伪单击，这是模仿人类点击的模仿，以完善细分面膜。