The advance of computer-aided detection systems using deep learning opened a new scope in endoscopic image analysis. However, the learning-based models developed on closed datasets are susceptible to unknown anomalies in complex clinical environments. In particular, the high false positive rate of polyp detection remains a major challenge in clinical practice. In this work, we release the FPPD-13 dataset, which provides a taxonomy and real-world cases of typical false positives during computer-aided polyp detection in real-world colonoscopy. We further propose a post-hoc module EndoBoost, which can be plugged into generic polyp detection models to filter out false positive predictions. This is realized by generative learning of the polyp manifold with normalizing flows and rejecting false positives through density estimation. Compared to supervised classification, this anomaly detection paradigm achieves better data efficiency and robustness in open-world settings. Extensive experiments demonstrate a promising false positive suppression in both retrospective and prospective validation. In addition, the released dataset can be used to perform 'stress' tests on established detection systems and encourages further research toward robust and reliable computer-aided endoscopic image analysis. The dataset and code will be publicly available at http://endoboost.miccai.cloud.

在辅助和自动驾驶系统的各种传感器中，即使在不利的天气或照明条件下，汽车雷达也被认为是一种健壮且低成本的解决方案。随着雷达技术的最新发展和开源的注释数据集，带有雷达信号的语义分割变得非常有前途。但是，现有的方法在计算上是昂贵的，或者通过平均将其减少到2D平面，从原始3D雷达信号中丢弃了大量的有价值的信息。在这项工作中，我们引入了Erase-Net，这是一个有效的雷达分割网络，以语义上的原始雷达信号。我们方法的核心是新型的检测到原始雷达信号的段方法。它首先检测每个对象的中心点，然后提取紧凑的雷达信号表示，最后执行语义分割。我们表明，与最新技术（SOTA）技术相比，我们的方法可以在雷达语义分割任务上实现卓越的性能。此外，我们的方法需要减少20倍的计算资源。最后，我们表明所提出的擦除网络可以被40％压缩而不会造成大幅损失，这比SOTA网络大得多，这使其成为实用汽车应用的更有希望的候选人。

您将如何通过一些错过来修复物理物体？您可能会想象它的原始形状从先前捕获的图像中，首先恢复其整体（全局）但粗大的形状，然后完善其本地细节。我们有动力模仿物理维修程序以解决点云完成。为此，我们提出了一个跨模式的形状转移双转化网络（称为CSDN），这是一种带有全循环参与图像的粗到精细范式，以完成优质的点云完成。 CSDN主要由“ Shape Fusion”和“ Dual-Refinect”模块组成，以应对跨模式挑战。第一个模块将固有的形状特性从单个图像传输，以指导点云缺失区域的几何形状生成，在其中，我们建议iPadain嵌入图像的全局特征和部分点云的完成。第二个模块通过调整生成点的位置来完善粗糙输出，其中本地改进单元通过图卷积利用了小说和输入点之间的几何关系，而全局约束单元则利用输入图像来微调生成的偏移。与大多数现有方法不同，CSDN不仅探讨了图像中的互补信息，而且还可以在整个粗到精细的完成过程中有效利用跨模式数据。实验结果表明，CSDN对十个跨模式基准的竞争对手表现出色。

在3D点云的一代任务中，点云完成越来越流行，因为从其部分观察结果中恢复了3D对象的完整形状是一个具有挑战性但必不可少的问题。在本文中，我们提出了一种新型的种子形式，以提高点云完成中细节保存和恢复的能力。与以前的基于全局特征向量的方法不同，我们引入了一种新的形状表示形式，即补丁种子，不仅可以从部分输入中捕获一般结构，而且还保留了本地模式的区域信息。然后，通过将种子特征集成到生成过程中，我们可以以粗到精细的方式恢复忠实的细节，以获取完整的点云。此外，我们通过将变压器结构扩展到点发生器的基本操作来设计上样本变压器，该结构有效地结合了相邻点之间的空间和语义关系。定性和定量评估表明，我们的方法在多个基准数据集上优于最先进的完成网络。我们的代码可从https://github.com/hrzhou2/seedformer获得。

3D点云的卷积经过广泛研究，但在几何深度学习中却远非完美。卷积的传统智慧在3D点之间表现出特征对应关系，这是对差的独特特征学习的内在限制。在本文中，我们提出了自适应图卷积（AGCONV），以供点云分析的广泛应用。 AGCONV根据其动态学习的功能生成自适应核。与使用固定/各向同性核的解决方案相比，AGCONV提高了点云卷积的灵活性，有效，精确地捕获了不同语义部位的点之间的不同关系。与流行的注意力体重方案不同，AGCONV实现了卷积操作内部的适应性，而不是简单地将不同的权重分配给相邻点。广泛的评估清楚地表明，我们的方法优于各种基准数据集中的点云分类和分割的最新方法。同时，AGCONV可以灵活地采用更多的点云分析方法来提高其性能。为了验证其灵活性和有效性，我们探索了基于AGCONV的完成，DeNoing，Upsmpling，注册和圆圈提取的范式，它们与竞争对手相当甚至优越。我们的代码可在https://github.com/hrzhou2/adaptconv-master上找到。

深度学习技术导致了通用对象检测领域的显着突破，近年来产生了很多场景理解的任务。由于其强大的语义表示和应用于场景理解，场景图一直是研究的焦点。场景图生成（SGG）是指自动将图像映射到语义结构场景图中的任务，这需要正确标记检测到的对象及其关系。虽然这是一项具有挑战性的任务，但社区已经提出了许多SGG方法并取得了良好的效果。在本文中，我们对深度学习技术带来了近期成就的全面调查。我们审查了138个代表作品，涵盖了不同的输入方式，并系统地将现有的基于图像的SGG方法从特征提取和融合的角度进行了综述。我们试图通过全面的方式对现有的视觉关系检测方法进行连接和系统化现有的视觉关系检测方法，概述和解释SGG的机制和策略。最后，我们通过深入讨论当前存在的问题和未来的研究方向来完成这项调查。本调查将帮助读者更好地了解当前的研究状况和想法。

乐器识别是广泛应用的音乐信息检索应用。由于以前的大多数乐器识别数据集专注于西方乐器，研究人员很难研究和评估传统的中国乐器识别领域。本文提出了传统的中国音乐数据集，用于培训模型和绩效评估，名为Chmusic。此数据集是免费且公开的，11名中国传统音乐仪器和55名繁体中文音乐摘录在此数据集中录制。然后基于Chmusic数据集提出了评估标准。通过本标准，研究人员可以按照相同规则进行比较它们的结果，不同的研究人员的结果将变得可比。

Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. We further collect human annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a graph-neural-network-based dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension abilities. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.

Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries. In this paper, we show that factual inconsistency can be caused by irrelevant parts of the input text, which act as confounders. To that end, we leverage information-theoretic measures of causal effects to quantify the amount of confounding and precisely quantify how they affect the summarization performance. Based on insights derived from our theoretical results, we design a simple multi-task model to control such confounding by leveraging human-annotated relevant sentences when available. Crucially, we give a principled characterization of data distributions where such confounding can be large thereby necessitating the use of human annotated relevant sentences to generate factual summaries. Our approach improves faithfulness scores by 20\% over strong baselines on AnswerSumm \citep{fabbri2021answersumm}, a conversation summarization dataset where lack of faithfulness is a significant issue due to the subjective nature of the task. Our best method achieves the highest faithfulness score while also achieving state-of-the-art results on standard metrics like ROUGE and METEOR. We corroborate these improvements through human evaluation.

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.