快速的现场评估（ROSE）技术可以通过适当地分析快速染色的细胞病理学图像来显着加速胰腺癌的诊断。计算机辅助诊断（CAD）可以潜在地解决玫瑰病中病理学家的短缺。但是，不同样品之间的癌性模式差异很大，这使CAD任务极具挑战性。此外，由于不同的染色质量和各种采集装置类型，玫瑰图像在颜色分布，亮度和对比度方面具有复杂的扰动。为了应对这些挑战，我们提出了一种基于随机实例的视觉变压器（SI-VIT）方法，该方法可以减少扰动并增强实例之间的建模。借助重新组装的洗牌实例及其行李级软标签，该方法利用回归头将模型集中在细胞上，而不是各种扰动。同时，该模型与分类头结合在一起，可以有效地识别不同实例之间的一般分布模式。结果表明，分类准确性有了更准确的注意区域的显着提高，表明玫瑰图像的多种模式有效地提取了，并且复杂的扰动大大降低。这也表明SI-VIT在分析细胞病理学图像方面具有巨大的潜力。代码和实验结果可在https://github.com/sagizty/mil-si上获得。

随着Covid-19的爆发，近年来已经出现了大量相关研究。我们提出了一个基于肺CT扫描图像的自动COVID-19诊断框架，即PVT-COV19D。为了适应图像输入的不同维度，我们首先使用变压器模型对图像进行了分类，然后根据正常分布对数据集中进行采样，并将采样结果馈送到修改的PVTV2模型中以进行训练。COV19-CT-DB数据集上的大量实验证明了该方法的有效性。

基于聚类的方法，在伪标签的产生和特征提取网络的优化之间交替，在无监督学习（USL）和无监督的域自适应（UDA）人重新识别（RE-ID）中起着主要作用。为了减轻嘈杂的伪标签的不利影响，现有方法要么放弃不可靠的标签，要么通过相互学习或标签传播来完善伪标签。但是，仍然积累了许多错误的标签，因为这些方法主要采用传统的无监督聚类算法，这些算法依赖于对数据分布的某些假设，并且无法捕获复杂的现实世界数据的分布。在本文中，我们提出了基于插件的伪标签校正网络（GLC），以以监督聚类的方式完善伪标签。训练GLC可以通过任何聚类方法生成的初始伪标签的监督来感知自训练的每个时期的不同数据分布。它可以学会通过K最近的邻居（KNN）图和早期训练策略的样本之间的关系约束来纠正初始嘈杂标签。具体而言，GLC学会从邻居汇总节点特征，并预测是否应在图上链接节点。此外，在对嘈杂的标签进行严重记忆以防止过度拟合嘈杂的伪标签之前，GLC已通过“早停”进行了优化。因此，尽管监督信号包含一些噪音，但GLC提高了伪标签的质量，从而可以更好地进行重新ID性能。在Market-1501和MSMT17上进行了USL和UDA人重新ID的广泛实验表明，我们的方法与各种基于聚类的方法广泛兼容，并始终如一地促进最先进的性能。

在这项工作中，我们在分配强化学习方面建立了最新的进步，以基于IQN提供模型的最新分配变体。我们通过使用GAN模型的生成器和鉴别器功能与分位数回归来实现这一目标，从而近似于状态返回分布的完整分位数。我们证明了基线数据集的性能提高-57 Atari 2600游戏。此外，我们使用算法来显示Atari游戏中风险敏感政策的最新培训表现，并通过政策优化和评估。

在亲自重新识别（REID）中，最近的研究已经验证了未标记的人图像上的模型的预训练要比ImageNet上要好得多。但是，这些研究直接应用了为图像分类设计的现有自我监督学习（SSL）方法，用于REID，而无需在框架中进行任何适应。这些SSL方法将本地视图的输出（例如红色T恤，蓝色短裤）与同时的全球视图相匹配，从而丢失了很多细节。在本文中，我们提出了一种特定于REID的预训练方法，部分意识的自我监督预训练（PASS），该方法可以生成零件级别的功能以提供细粒度的信息，并且更适合REID。通行证将图像分为几个局部区域，每个区域随机裁剪的本地视图都有特定的可学习[部分]令牌。另一方面，所有地方区域的[部分]也附加到全球视图中。通行证学习以匹配同一[部分]上本地视图的输出和全局视图。也就是说，从本地区域获得的本地视图的[部分]仅与从全球视图中学到的相应[部分]相匹配。结果，每个[部分]可以专注于图像的特定局部区域，并提取该区域的细粒度信息。实验显示通行证在Market1501和MSMT17上的新最先进的表演以及各种REID任务（例如Vanilla vit-s/16）通过Pass Achieves 92.2 \％/90.2 \％/88.5 \％地图准确性，例如Vanilla vit-s/16在Market1501上进行监督/UDA/USL REID。我们的代码可在https://github.com/casia-iva-lab/pass-reid上找到。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

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.

Learning feature interactions is the key to success for the large-scale CTR prediction and recommendation. In practice, handcrafted feature engineering usually requires exhaustive searching. In order to reduce the high cost of human efforts in feature engineering, researchers propose several deep neural networks (DNN)-based approaches to learn the feature interactions in an end-to-end fashion. However, existing methods either do not learn both vector-wise interactions and bit-wise interactions simultaneously, or fail to combine them in a controllable manner. In this paper, we propose a new model, xDeepInt, based on a novel network architecture called polynomial interaction network (PIN) which learns higher-order vector-wise interactions recursively. By integrating subspace-crossing mechanism, we enable xDeepInt to balance the mixture of vector-wise and bit-wise feature interactions at a bounded order. Based on the network architecture, we customize a combined optimization strategy to conduct feature selection and interaction selection. We implement the proposed model and evaluate the model performance on three real-world datasets. Our experiment results demonstrate the efficacy and effectiveness of xDeepInt over state-of-the-art models. We open-source the TensorFlow implementation of xDeepInt: https://github.com/yanyachen/xDeepInt.

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.