Most existing person re-identification methods compute the matching relations between person images across camera views based on the ranking of the pairwise similarities. This matching strategy with the lack of the global viewpoint and the context's consideration inevitably leads to ambiguous matching results and sub-optimal performance. Based on a natural assumption that images belonging to the same person identity should not match with images belonging to multiple different person identities across views, called the unicity of person matching on the identity level, we propose an end-to-end person unicity matching architecture for learning and refining the person matching relations. First, we adopt the image samples' contextual information in feature space to generate the initial soft matching results by using graph neural networks. Secondly, we utilize the samples' global context relationship to refine the soft matching results and reach the matching unicity through bipartite graph matching. Given full consideration to real-world person re-identification applications, we achieve the unicity matching in both one-shot and multi-shot settings of person re-identification and further develop a fast version of the unicity matching without losing the performance. The proposed method is evaluated on five public benchmarks, including four multi-shot datasets MSMT17, DukeMTMC, Market1501, CUHK03, and a one-shot dataset VIPeR. Experimental results show the superiority of the proposed method on performance and efficiency.

激活函数是元素的数学函数，在深神经网络（DNN）中起着至关重要的作用。已经提出了许多新颖和复杂的激活功能来提高DNN的准确性，但在训练过程中还可以通过反向传播消耗大量记忆。在这项研究中，我们提出了嵌套的正向自动分化（正向AD），专门针对用于记忆效率的DNN训练的元素激活函数。我们在两个广泛使用的深度学习框架（Tensorflow和Pytorch）中部署了嵌套的AD，分别支持静态和动态计算图。我们的评估表明，在相同的记忆降低率下，嵌套的前AD嵌套将记忆足迹降低到1.97倍，比基线模型降低了20％。

我们建议一个基于深入强化学习的经理工作框架，以解决旅行推销员问题（TSP）的艰难而又非平凡的变体，\ ie〜有时间窗口和拒绝（MTSPTWR）的多车辆TSP（MTSPTWR），在此之前无法服务的客户截止日期将受到拒绝。特别是，在拟议的框架中，经理代理人通过基于图形同构网络（GIN）的策略网络将客户分配给每辆车，从而将MTSPTWR分为子路由任务。工人代理人通过根据每辆车的旅行长度和拒绝率来最大程度地降低成本来解决子路由任务，然后将其最多的最大值送回经理代理以学习更好的任务。实验结果表明，所提出的框架在更高的解决方案质量和较短的计算时间方面优于强基础。更重要的是，训练有素的代理商还取得了竞争性能，以解决看不见的较大实例。

网络对齐（NA）是在不同网络上发现节点对应关系的任务。尽管NA方法在无数场景中取得了巨大的成功，但它们的令人满意的性能并非没有先前的锚链接信息和/或节点属性，这可能并不总是可用。在本文中，我们提出了一种使用节点属性增强的新型NA方法的Grad-Align+，对于没有此类其他信息，它非常健壮。 Grad-Align+建立在最近的最新NA方法（所谓的Grad-Align）上，该方法逐渐发现了节点对的一部分，直到找到所有节点对。具体而言，grad Align+由以下关键组成组成：1）基于节点的中心度度量的增强节点属性，2）计算从图神经网络中提取的嵌入相似性矩阵，并在该图中提取了增强节点属性，并在其中进食增强的节点属性和3）通过计算相对于对齐的跨网络邻域对，逐渐发现节点对。实验结果表明，Grad-Align+具有（a）优于基准NA方法的优势，（b）我们理论发现的经验验证，以及（c）我们属性增强模块的有效性。

点击率（CTR）预测任务是推荐系统中的基本任务。以前的大多数CTR模型研究基于宽\和深层结构构建，并逐渐演变为具有不同模块的平行结构。但是，平行结构的简单积累会导致更高的结构复杂性和更长的训练时间。基于输出层的Sigmoid激活函数，训练过程中平行结构的线性添加激活值很容易使样品落入弱梯度间隔，从而导致弱梯度的现象，并降低训练的有效性。为此，本文提出了一个平行的异质网络（PHN）模型，该模型通过三种不同的交互分析方法构建具有并行结构的网络，并使用软选择门控（SSG）以具有不同结构的异质数据。最后，在网络中使用了与可训练参数的残留链接来减轻弱梯度现象的影响。此外，我们证明了PHN在大量比较实验中的有效性，并可视化模型在训练过程和结构中的性能。

强大的语义细分面临的一个普遍挑战是昂贵的数据注释成本。现有的半监督解决方案显示出解决此问题的巨大潜力。他们的关键想法是通过未经监督的数据增加未标记的数据来构建一致性正则化，以进行模型培训。未标记数据的扰动使一致性训练损失使半监督的语义分割受益。但是，这些扰动破坏了图像上下文并引入了不自然的边界，这对语义分割是有害的。此外，广泛采用的半监督学习框架，即均值老师，遭受了绩效限制，因为学生模型最终会收敛于教师模型。在本文中，首先，我们提出了一个友好的可区分几何扭曲，以进行无监督的数据增强。其次，提出了一个新颖的对抗双重学生框架，以从以下两个方面从以下两个方面改善均等老师：（1）双重学生模型是独立学习的，除了稳定约束以鼓励利用模型多样性； （2）对对抗性训练计划适用于学生，并诉诸歧视者以区分无标记数据的可靠伪标签进行自我训练。通过对Pascal VOC2012和CityScapes进行的广泛实验来验证有效性。我们的解决方案可显着提高两个数据集的性能和最先进的结果。值得注意的是，与完全监督相比，我们的解决方案仅使用Pascal VOC2012上的12.5％注释数据获得了73.4％的可比MIOU。我们的代码和模型可在https://github.com/caocong/ads-semiseg上找到。

变压器在各种计算机视觉地区发挥着越来越重要的作用，并且在点云分析中也取得了显着的成就。由于它们主要专注于点亮变压器，因此本文提出了一种自适应通道编码变压器。具体地，被设计为对频道的通道卷积旨在对信道进行编码。它可以通过捕获坐标和特征之间的潜在关系来编码特征通道。与简单地为每个通道分配注意重量相比，我们的方法旨在自适应地对信道进行编码。此外，我们的网络采用了邻域搜索方法的低级和高级双语义接收领域，以提高性能。广泛的实验表明，我们的方法优于三个基准数据集的最先进的点云分类和分段方法。

With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed. In this paper, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss the physical model, datasets, network modules, loss functions, and evaluation metrics that are commonly used. Then, the main contributions of various dehazing algorithms are categorized and summarized. Further, quantitative and qualitative experiments of various baseline methods are carried out. Finally, the unsolved issues and challenges that can inspire the future research are pointed out. A collection of useful dehazing materials is available at \url{https://github.com/Xiaofeng-life/AwesomeDehazing}.

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.