Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignment task as a Wasserstein-Procrustes problem. However, linear programming algorithms and approximate OT solvers via Sinkhorn for computing the permutation matrix come with a significant computational burden since they scale cubically and quadratically, respectively, in the input size. This makes it slow and infeasible to compute OT distances exactly for a larger input size, resulting in a poor approximation quality of the permutation matrix and subsequently a less robust learned transfer function or mapper. This paper proposes an unsupervised projection-based CLWE model called quantized Wasserstein Procrustes (qWP). qWP relies on a quantization step of both the source and target monolingual embedding space to estimate the permutation matrix given a cheap sampling procedure. This approach substantially improves the approximation quality of empirical OT solvers given fixed computational cost. We demonstrate that qWP achieves state-of-the-art results on the Bilingual lexicon Induction (BLI) task.

智能城市应用程序（例如智能交通路由或事故预防）依赖计算机视觉方法来确切的车辆定位和跟踪。由于精确标记的数据缺乏，从多个摄像机中检测和跟踪3D的车辆被证明是探索挑战的。我们提出了一个庞大的合成数据集，用于多个重叠和非重叠摄像头视图中的多个车辆跟踪和分割。与现有的数据集不同，该数据集仅为2D边界框提供跟踪地面真实，我们的数据集还包含适用于相机和世界坐标中的3D边界框的完美标签，深度估计以及实例，语义和泛型细分。该数据集由17个小时的标记视频材料组成，从64个不同的一天，雨，黎明和夜幕播放的340张摄像机录制，使其成为迄今为止多目标多型多相机跟踪的最广泛数据集。我们提供用于检测，车辆重新识别以及单摄像机跟踪的基准。代码和数据公开可用。

图形神经网络（GNN）是专门为图形数据设计的深度学习模型，它们通常依靠节点特征作为第一层的输入。在没有节点功能的图形上应用这种类型的网络时，可以提取基于图的节点特征（例如，度数数）或在训练网络时学习输入节点表示（即嵌入）。训练节点嵌入的后一个方法更有可能导致性能更好，而与嵌入的参数数量与节点数量线性增长。因此，在处理工业规模的图形数据时，以端到端方式以端到端方式训练输入节点嵌入式（GPU）内存中的GNN是不切实际的。受到为自然语言处理（NLP）任务开发的嵌入压缩方法的启发，我们开发了一种节点嵌入压缩方法，其中每个节点都用一个位向量而不是浮点数向量表示。在压缩方法中使用的参数可以与GNN一起训练。我们表明，与替代方案相比，提出的节点嵌入压缩方法的性能优于性能。

矩阵配置文件是一种有效的数据挖掘工具，可提供时间序列数据的相似关系。矩阵配置文件的用户可以使用相似性连接（即，自行连接）或使用相似性相互作用连接使用另一个时间序列加入时间序列。通过调用或两种类型的连接，矩阵配置文件可以帮助用户在数据中发现保守和异常结构。自从五年前引入矩阵简介以来，已经进行了多项努力，以加快近似联合的计算;然而，大多数这些努力只关注自我连接。在这项工作中，我们表明可以通过创建时间序列的紧凑“字典”表示，有效地使用误差限制保证来执行近似时间序列相似度。使用字典表示而不是原始时间序列，我们能够将异常挖掘系统的吞吐量至少为20倍，基本上没有准确度降低。作为副作用，字典还以语义有意义的方式总结时间序列，可以提供直观和可操作的见解。我们展示了我们的字典的内部序列相似性的实用性，如医学和运输所多样化的域。

The most useful data mining primitives are distance measures. With an effective distance measure, it is possible to perform classification, clustering, anomaly detection, segmentation, etc. For single-event time series Euclidean Distance and Dynamic Time Warping distance are known to be extremely effective. However, for time series containing cyclical behaviors, the semantic meaningfulness of such comparisons is less clear. For example, on two separate days the telemetry from an athlete workout routine might be very similar. The second day may change the order in of performing push-ups and squats, adding repetitions of pull-ups, or completely omitting dumbbell curls. Any of these minor changes would defeat existing time series distance measures. Some bag-of-features methods have been proposed to address this problem, but we argue that in many cases, similarity is intimately tied to the shapes of subsequences within these longer time series. In such cases, summative features will lack discrimination ability. In this work we introduce PRCIS, which stands for Pattern Representation Comparison in Series. PRCIS is a distance measure for long time series, which exploits recent progress in our ability to summarize time series with dictionaries. We will demonstrate the utility of our ideas on diverse tasks and datasets.

Most existing methods realize 3D instance segmentation by extending those models used for 3D object detection or 3D semantic segmentation. However, these non-straightforward methods suffer from two drawbacks: 1) Imprecise bounding boxes or unsatisfactory semantic predictions limit the performance of the overall 3D instance segmentation framework. 2) Existing method requires a time-consuming intermediate step of aggregation. To address these issues, this paper proposes a novel end-to-end 3D instance segmentation method based on Superpoint Transformer, named as SPFormer. It groups potential features from point clouds into superpoints, and directly predicts instances through query vectors without relying on the results of object detection or semantic segmentation. The key step in this framework is a novel query decoder with transformers that can capture the instance information through the superpoint cross-attention mechanism and generate the superpoint masks of the instances. Through bipartite matching based on superpoint masks, SPFormer can implement the network training without the intermediate aggregation step, which accelerates the network. Extensive experiments on ScanNetv2 and S3DIS benchmarks verify that our method is concise yet efficient. Notably, SPFormer exceeds compared state-of-the-art methods by 4.3% on ScanNetv2 hidden test set in terms of mAP and keeps fast inference speed (247ms per frame) simultaneously. Code is available at https://github.com/sunjiahao1999/SPFormer.

具有更多参数数量的深卷积神经网络在自然图像上的对象检测任务中提高了精度，其中感兴趣的对象用水平边界框注释。从鸟类视角捕获的航空图像上，这些对模型架构和更深卷积层的改进也可以提高定向对象检测任务的性能。但是，很难直接在设备上使用有限的计算资源应用那些最先进的对象探测器，这需要通过模型压缩来实现轻量级模型。为了解决此问题，我们提出了一种模型压缩方法，用于通过知识蒸馏（即KD-RNET）在空中图像上旋转对象检测。凭借具有大量参数的训练有素的以教师为导向的对象探测器，获得的对象类别和位置信息都通过协作培训策略转移到KD-RNET的紧凑型学生网络中。传输类别信息是通过对预测概率分布的知识蒸馏来实现的，并且在处理位置信息传输中的位移时采用了软回归损失。大规模空中对象检测数据集（DOTA）的实验结果表明，提出的KD-RNET模型可以通过减少参数数量来提高均值平均精度（MAP），同时kd-rnet促进性能增强性能在提供高质量检测的情况下，与地面截然注释的重叠更高。

由于鸟瞰视角的任意对象方向和复杂的背景，航空图像中的船舶检测仍然是一个活跃但具有挑战性的任务。大多数现有方法依赖于角度预测或预定义的锚盒，使这些方法对不稳定的角度回归和过度的超参数设置非常敏感。为了解决这些问题，我们用锚角和角度范例替换基于角的对象编码，并提出了一种部署中心的新型探测器，用于编码每个定向对象的四个中点，即MIDnet。 MIDNET设计用于增强船舶中点的对称可变形卷积，然后通过预测相应的离心移位和匹配半径来自适应地匹配相同的船的中心和中点。最后，提出了一种简洁的分析几何算法，以逐步优化中心和中点 - 明智地为建立精确定向的边界盒。在两艘公共船舶检测数据集，HRSC2016和FGSD2021，MIDNet通过实现90.52％和86.50％的AP来实现最先进的探测器。此外，MIDNET在DotA的船舶检测中获得竞争结果。

在这项工作中，我们呈现了DCC（更深层兼容的压缩），用于实时无人机的辅助边缘辅助视频分析的一个启用技术，内置于现有编解码器之上。DCC解决了一个重要的技术问题，以将流动的视频从无人机压缩到边缘，而不会严格地在边缘执行的视频分析任务的准确性和及时性。DCC通过流式视频中的每一位对视频分析同样有价值，这是对视频分析的同样有价值，这在传统的分析透视技术编解码器技术上打开了新的压缩室。我们利用特定的无人机的上下文和中级提示，从物体检测中追求保留分析质量所需的自适应保真度。我们在一个展示车辆检测应用中有原型DCC，并验证了其代表方案的效率。DCC通过基线方法减少9.5倍，在最先进的检测精度上，19-683％的速度减少了9.5倍。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.