Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining methods have two problems as follows. 1) In the hardness measurement, the important structural information is overlooked for similarity calculation, degrading the representativeness of the selected hard negative samples. 2) Previous works merely focus on the hard negative sample pairs while neglecting the hard positive sample pairs. Nevertheless, samples within the same cluster but with low similarity should also be carefully learned. To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample, thus improving the discriminative capability of the samples further. Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method.

低光环境对强大的无人驾驶汽车（UAV）跟踪也构成了巨大的挑战，即使使用最新的（SOTA）跟踪器，由于潜在的图像特征在不利的光条件下很难提取。此外，由于可见性较低，人类监视器的准确在线选择也极为难以在地面控制站中初始化无人机跟踪。为了解决这些问题，这项工作提出了一个新颖的增强剂，即凸线网，以点燃人类操作员和无人机跟踪器的潜在对象。通过采用变压器，LightlightNet可以根据全局特征调整增强参数，因此可以适应照明变化。引入了像素级范围掩模，以使光明网络更加专注于没有光源的跟踪对象和区域的增强。此外，建立了一种软截断机制，以防止背景噪声被误认为关键特征。对图像增强基准测试的评估表明，光明网络在促进人类感知方面具有优势。公共Uavdark135基准进行的实验表明，HightlightNet比其他SOTA低光增强剂更适合无人机跟踪任务。此外，在典型的无人机平台上进行的现实世界测试验证了HightlightNet在夜间航空跟踪相关应用中的实用性和效率。代码和演示视频可在https://github.com/vision4robotics/highlightnet上找到。

基于变压器的视觉对象跟踪已广泛使用。但是，变压器结构缺乏足够的电感偏差。此外，仅专注于编码全局功能会损害建模本地细节，这限制了航空机器人中跟踪的能力。具体而言，通过局部模型为全球搜索机制，提出的跟踪器将全局编码器替换为新型的局部识别编码器。在使用的编码器中，仔细设计了局部识别的关注和局部元素校正网络，以减少全局冗余信息干扰和增加局部归纳偏见。同时，后者可以通过详细信息网络准确地在空中视图下对本地对象详细信息进行建模。所提出的方法在几种权威的空中基准中实现了竞争精度和鲁棒性，总共有316个序列。拟议的跟踪器的实用性和效率已通过现实世界测试得到了验证。

领先的图对比度学习（GCL）方法在两个时尚中执行图形增强：（1）随机损坏锚图，这可能会导致语义信息的丢失，或（2）使用域知识维护显着特征，这破坏了对概括的概括其他域。从不变性看GCL时，我们认为高性能的增强应保留有关实例歧视的锚图的显着语义。为此，我们将GCL与不变的理由发现联系起来，并提出了一个新的框架，即理由吸引图形对比度学习（RGCL）。具体而言，没有监督信号，RGCL使用基本原理生成器来揭示有关图形歧视的显着特征作为理由，然后为对比度学习创建理由吸引的视图。这种理由意识到的预训练方案赋予了骨干模型具有强大的表示能力，从而进一步促进了下游任务的微调。在MNIST-SUPERPIXEL和MUTAG数据集上，对发现的理由的视觉检查展示了基本原理生成器成功捕获了显着特征（即区分图中的语义节点）。在生化分子和社交网络基准数据集上，RGCL的最新性能证明了理由意识到对比度学习的有效性。我们的代码可在https://github.com/lsh0520/rgcl上找到。

精确的心脏计算，多种式图像的分析和建模对于心脏病的诊断和治疗是重要的。晚期钆增强磁共振成像（LGE MRI）是一种有希望的技术，可视化和量化心肌梗塞（MI）和心房疤痕。由于LGE MRI的低图像质量和复杂的增强图案，MI和心房疤痕的自动化量可能是具有挑战性的。此外，与带金标准标签的其他序列LGE MRIS相比特别有限，这表示用于开发用于自动分割和LGE MRIS定量的新型算法的另一个障碍。本章旨在总结最先进的基于深度学习的多模态心脏图像分析的先进贡献。首先，我们向基于多序心脏MRI的心肌和病理分割介绍了两个基准工作。其次，提出了两种新的左心房瘢痕分割和从LGE MRI定量的新型框架。第三，我们为跨型心脏图像分割提出了三种无监督的域适应技术。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction. It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering and recommendation systems, etc. According to the graph types, the existing KGR models can be roughly divided into three categories, \textit{i.e.,} static models, temporal models, and multi-modal models. The early works in this domain mainly focus on static KGR and tend to directly apply general knowledge graph embedding models to the reasoning task. However, these models are not suitable for more complex but practical tasks, such as inductive static KGR, temporal KGR, and multi-modal KGR. To this end, multiple works have been developed recently, but no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a survey for knowledge graph reasoning tracing from static to temporal and then to multi-modal KGs. Concretely, the preliminaries, summaries of KGR models, and typical datasets are introduced and discussed consequently. Moreover, we discuss the challenges and potential opportunities. The corresponding open-source repository is shared on GitHub: https://github.com/LIANGKE23/Awesome-Knowledge-Graph-Reasoning.

Graph contrastive learning is an important method for deep graph clustering. The existing methods first generate the graph views with stochastic augmentations and then train the network with a cross-view consistency principle. Although good performance has been achieved, we observe that the existing augmentation methods are usually random and rely on pre-defined augmentations, which is insufficient and lacks negotiation between the final clustering task. To solve the problem, we propose a novel Graph Contrastive Clustering method with the Learnable graph Data Augmentation (GCC-LDA), which is optimized completely by the neural networks. An adversarial learning mechanism is designed to keep cross-view consistency in the latent space while ensuring the diversity of augmented views. In our framework, a structure augmentor and an attribute augmentor are constructed for augmentation learning in both structure level and attribute level. To improve the reliability of the learned affinity matrix, clustering is introduced to the learning procedure and the learned affinity matrix is refined with both the high-confidence pseudo-label matrix and the cross-view sample similarity matrix. During the training procedure, to provide persistent optimization for the learned view, we design a two-stage training strategy to obtain more reliable clustering information. Extensive experimental results demonstrate the effectiveness of GCC-LDA on six benchmark datasets.

Knowledge graph embedding (KGE) aims to learn powerful representations to benefit various artificial intelligence applications, such as question answering and recommendations. Meanwhile, contrastive learning (CL), as an effective mechanism to enhance the discriminative capacity of the learned representations, has been leveraged in different fields, especially graph-based models. However, since the structures of knowledge graphs (KGs) are usually more complicated compared to homogeneous graphs, it is hard to construct appropriate contrastive sample pairs. In this paper, we find that the entities within a symmetrical structure are usually more similar and correlated. This key property can be utilized to construct contrastive positive pairs for contrastive learning. Following the ideas above, we propose a relational symmetrical structure based knowledge graph contrastive learning framework, termed KGE-SymCL, which leverages the symmetrical structure information in KGs to enhance the discriminative ability of KGE models. Concretely, a plug-and-play approach is designed by taking the entities in the relational symmetrical positions as the positive samples. Besides, a self-supervised alignment loss is used to pull together the constructed positive sample pairs for contrastive learning. Extensive experimental results on benchmark datasets have verified the good generalization and superiority of the proposed framework.

近年来，图形神经网络（GNNS）在半监督节点分类中实现了有希望的性能。但是，监督不足的问题以及代表性崩溃，在很大程度上限制了GNN在该领域的性能。为了减轻半监督场景中节点表示的崩溃，我们提出了一种新型的图形对比学习方法，称为混合图对比度网络（MGCN）。在我们的方法中，我们通过扩大决策边界的边距并提高潜在表示的跨视图一致性来提高潜在特征的歧视能力。具体而言，我们首先采用了基于插值的策略来在潜在空间中进行数据增强，然后迫使预测模型在样本之间进行线性更改。其次，我们使学习的网络能够通过强迫跨视图的相关矩阵近似身份矩阵来分开两个插值扰动视图的样品。通过结合两个设置，我们从丰富的未标记节点和罕见但有价值的标记节点中提取丰富的监督信息，以进行判别表示学习。六个数据集的广泛实验结果证明了与现有最​​新方法相比，MGCN的有效性和普遍性。