Forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF; EC for short) can provide a basis for the establishment of maritime-disaster warning systems, but they contain some systematic biases.The fifth-generation EC atmospheric reanalysis (ERA5) data have high accuracy, but are delayed by about 5 days. To overcome this issue, a spatiotemporal deep-learning method could be used for nonlinear mapping between EC and ERA5 data, which would improve the quality of EC wind forecast data in real time. In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved double-encoder forecaster architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model. The study area was the western North Pacific (WNP), and real-time rolling bias corrections were made for 10-day wind-field forecasts released by the EC between December 2020 and November 2021, divided into four seasons. Compared with the original EC forecasts, after correction using the MT-DETrajGRU model the wind speed and wind direction biases in the four seasons were reduced by 8-11% and 9-14%, respectively. In addition, the proposed method modelled the data uniformly under different weather conditions. The correction performance under normal and typhoon conditions was comparable, indicating that the data-driven mode constructed here is robust and generalizable.

Ultra-fine entity typing (UFET) predicts extremely free-formed types (e.g., president, politician) of a given entity mention (e.g., Joe Biden) in context. State-of-the-art (SOTA) methods use the cross-encoder (CE) based architecture. CE concatenates the mention (and its context) with each type and feeds the pairs into a pretrained language model (PLM) to score their relevance. It brings deeper interaction between mention and types to reach better performance but has to perform N (type set size) forward passes to infer types of a single mention. CE is therefore very slow in inference when the type set is large (e.g., N = 10k for UFET). To this end, we propose to perform entity typing in a recall-expand-filter manner. The recall and expand stages prune the large type set and generate K (K is typically less than 256) most relevant type candidates for each mention. At the filter stage, we use a novel model called MCCE to concurrently encode and score these K candidates in only one forward pass to obtain the final type prediction. We investigate different variants of MCCE and extensive experiments show that MCCE under our paradigm reaches SOTA performance on ultra-fine entity typing and is thousands of times faster than the cross-encoder. We also found MCCE is very effective in fine-grained (130 types) and coarse-grained (9 types) entity typing. Our code is available at \url{https://github.com/modelscope/AdaSeq/tree/master/examples/MCCE}.

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the world's largest single-dish radio telescope. Its large reflecting surface achieves unprecedented sensitivity but is prone to damage, such as dents and holes, caused by naturally-occurring falling objects. Hence, the timely and accurate detection of surface defects is crucial for FAST's stable operation. Conventional manual inspection involves human inspectors climbing up and examining the large surface visually, a time-consuming and potentially unreliable process. To accelerate the inspection process and increase its accuracy, this work makes the first step towards automating the inspection of FAST by integrating deep-learning techniques with drone technology. First, a drone flies over the surface along a predetermined route. Since surface defects significantly vary in scale and show high inter-class similarity, directly applying existing deep detectors to detect defects on the drone imagery is highly prone to missing and misidentifying defects. As a remedy, we introduce cross-fusion, a dedicated plug-in operation for deep detectors that enables the adaptive fusion of multi-level features in a point-wise selective fashion, depending on local defect patterns. Consequently, strong semantics and fine-grained details are dynamically fused at different positions to support the accurate detection of defects of various scales and types. Our AI-powered drone-based automated inspection is time-efficient, reliable, and has good accessibility, which guarantees the long-term and stable operation of FAST.

Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns; a multisize self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN.

Out-Of-Distribution (OOD) detection has received broad attention over the years, aiming to ensure the reliability and safety of deep neural networks (DNNs) in real-world scenarios by rejecting incorrect predictions. However, we notice a discrepancy between the conventional evaluation vs. the essential purpose of OOD detection. On the one hand, the conventional evaluation exclusively considers risks caused by label-space distribution shifts while ignoring the risks from input-space distribution shifts. On the other hand, the conventional evaluation reward detection methods for not rejecting the misclassified image in the validation dataset. However, the misclassified image can also cause risks and should be rejected. We appeal to rethink OOD detection from a human-centric perspective, that a proper detection method should reject the case that the deep model's prediction mismatches the human expectations and adopt the case that the deep model's prediction meets the human expectations. We propose a human-centric evaluation and conduct extensive experiments on 45 classifiers and 8 test datasets. We find that the simple baseline OOD detection method can achieve comparable and even better performance than the recently proposed methods, which means that the development in OOD detection in the past years may be overestimated. Additionally, our experiments demonstrate that model selection is non-trivial for OOD detection and should be considered as an integral of the proposed method, which differs from the claim in existing works that proposed methods are universal across different models.

在模板和搜索区域之间学习强大的功能匹配对于3D暹罗跟踪至关重要。暹罗功能匹配的核心是如何在模板和搜索区域之间的相应点上分配高特征相似性，以进行精确的对象本地化。在本文中，我们提出了一个新颖的点云登记驱动的暹罗跟踪框架，直觉是空间对齐相应点（通过3D注册）倾向于实现一致的特征表示。具体而言，我们的方法由两个模块组成，包括特定于特定的非局部注册模块和一个注册辅助的sindhorn模板 - 特征聚合模块。登记模块在模板和搜索区域之间的精确空间对齐中进行目标。提出了跟踪特异性的空间距离约束，以优化非局部模块中的交叉注意权重，以进行判别特征学习。然后，我们使用加权SVD来计算模板和搜索区域之间的刚性转换，并对齐它们以实现所需的空间对齐相应点。对于特征聚合模型，我们将转换模板和搜索区域之间的特征匹配作为最佳传输问题，并利用Sinkhorn优化来搜索异常型匹配匹配解决方案。同样，建造了登记辅助空间距离图，以改善无法区分的区域（例如光滑的表面）的匹配鲁棒性。最后，在获得的功能匹配地图的指导下，我们将目标信息从模板中汇总到搜索区域中以构建特定于目标的特征，然后将其馈送到一个类似中心点的检测头中以进行对象定位。关于Kitti，Nuscenes和Waymo数据集的广泛实验验证了我们提出的方法的有效性。

小鼠的自动社会行为分析已成为行为神经科学中越来越流行的研究领域。最近，已使用姿势信息（即关键点或骨骼的位置）来解释小鼠的社会行为。然而，很少在现有方法中研究了小鼠关键点基础的社会互动信息的有效编码和解码。特别是，由于高度变形的身体形状和模棱两可的运动模式，建模小鼠之间复杂的社交互动是一项挑战。为了处理交互建模问题，我们在这里提出了一个跨骨骼相互作用图聚合网络（CS-IGANET），以学习自由相互作用的小鼠的丰富动力学，其中使用了跨骨骼节点级交互模块（CS-NLI）建模多级相互作用（即内部，间和跨骨骼相互作用）。此外，我们设计了一种新颖的互动感知变压器（IAT），以动态学习社交行为的图形表示，并更新节点级表示，并在我们提出的互动意识到的自我注意力下的机制的指导下。最后，为了增强我们的模型的表示能力，提出了辅助自我监督的学习任务来衡量跨骨骼节点之间的相似性。标准CRMI13-SKERTON和我们的PDMB-Skeleton数据集的实验结果表明，我们所提出的模型的表现优于其他几种最先进的方法。

对比学习在图表学习领域表现出了巨大的希望。通过手动构建正/负样本，大多数图对比度学习方法依赖于基于矢量内部产品的相似性度量标准来区分图形表示样品。但是，手工制作的样品构建（例如，图表的节点或边缘的扰动）可能无法有效捕获图形的固有局部结构。同样，基于矢量内部产品的相似性度量标准无法完全利用图形的局部结构来表征图差。为此，在本文中，我们提出了一种基于自适应子图生成的新型对比度学习框架，以实现有效且强大的自我监督图表示学习，并且最佳传输距离被用作子绘图之间的相似性度量。它的目的是通过捕获图的固有结构来生成对比样品，并根据子图的特征和结构同时区分样品。具体而言，对于每个中心节点，通过自适应学习关系权重与相应邻域的节点，我们首先开发一个网络来生成插值子图。然后，我们分别构建来自相同和不同节点的子图的正和负对。最后，我们采用两种类型的最佳运输距离（即Wasserstein距离和Gromov-Wasserstein距离）来构建结构化的对比损失。基准数据集上的广泛节点分类实验验证了我们的图形对比学习方法的有效性。

本文介绍了Davarocr，这是一种用于OCR和文档理解任务的开源工具箱。Davarocr目前实施19种高级算法，涵盖9个不同的任务表。Davarocr为每种算法提供了详细的用法说明和经过训练的模型。与以前的OpenSource OCR工具箱相比，Davarocr对文档理解的尖端技术的子任务具有相对完整的支持。为了促进OCR技术在学术界和行业中的开发和应用，我们更加关注使用不同的技术可以共享的模块的使用。Davarocr在https://github.com/hikopensource/davar-lab-ocr上公开发行。

RGB-thermal显着对象检测（RGB-T SOD）旨在定位对齐可见的和热红外图像对的共同突出对象，并准确地分割所有属于这些对象的像素。由于对热图像的照明条件不敏感，它在诸如夜间和复杂背景之类的具有挑战性的场景中很有希望。因此，RGB-T SOD的关键问题是使两种方式的功能相互补充并互相调整，因为不可避免的是，由于极端光条件和诸如极端光条件和诸如极端光明条件和热跨界。在本文中，我们提出了一个针对RGB-T SOD的新型镜子互补变压器网络（MCNET）。具体而言，我们将基于变压器的特征提取模块引入RGB和热图像的有效提取分层特征。然后，通过基于注意力的特征相互作用和基于串行的多尺度扩张卷积（SDC）特征融合模块，提出的模型实现了低级特征的互补相互作用以及深度特征的语义融合。最后，基于镜子互补结构，即使是一种模态也可以准确地提取两种方式的显着区域也是无效的。为了证明在现实世界中具有挑战性的场景下提出的模型的鲁棒性，我们基于自动驾驶域中使用的大型公共语义分段RGB-T数据集建立了一种新颖的RGB-T SOD数据集VT723。基准和VT723数据集上的昂贵实验表明，所提出的方法优于最先进的方法，包括基于CNN的方法和基于变压器的方法。该代码和数据集将在稍后在https://github.com/jxr326/swinmcnet上发布。