The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

变压器的扎实结果使它们在各种自然语言和视觉任务中占上风。作为变压器中的默认组件，层归一化（LN）将每个令牌内的激活归一化，以增强稳健性。但是，LN需要在推理以及除法和平方根操作中进行直接统计计算，从而导致硬件效率低下。更重要的是，用其他硬件有效的标准化方案（例如，批发归一化）代替LN会导致性能较低，甚至在训练中崩溃。我们发现，这种困境是由激活统计的异常行为引起的，包括对迭代的大波动和跨层的极端异常值。为了解决这些问题，我们提出了统一的归一化（UN），可以通过与其他线性操作融合并在LN上实现可比性的性能来加快推断。联合国通过量身定制的波动平滑策略校准激活和梯度统计来努力提高性能。同时，采用自适应离群过滤策略来避免在本文中在理论上证明并在实验上验证的训练中崩溃。我们证明，通过对语言和视觉任务进行广泛的实验，联合国可以成为LN的有效替代品。此外，我们评估了我们方法在GPU上的效率。配备了联合国的变压器享受约31％的推理速度和近18％的记忆力减少。代码将在https://github.com/hikvision-research/unified-normalization上发布。

近年来，随着新颖的策略和应用，神经网络一直在迅速扩展。然而，尽管不可避免地会针对关键应用程序来解决这些挑战，例如神经网络技术诸如神经网络技术中仍未解决诸如神经网络技术的挑战。已经尝试通过用符号表示来表示和嵌入域知识来克服神经网络计算中的挑战。因此，出现了神经符号学习（Nesyl）概念，其中结合了符号表示的各个方面，并将常识带入神经网络（Nesyl）。在可解释性，推理和解释性至关重要的领域中，例如视频和图像字幕，提问和推理，健康信息学和基因组学，Nesyl表现出了有希望的结果。这篇综述介绍了一项有关最先进的Nesyl方法的全面调查，其原理，机器和深度学习算法的进步，诸如Opthalmology之类的应用以及最重要的是该新兴领域的未来观点。

时间网络链接预测是网络科学领域的重要任务，并且在实际情况下具有广泛的应用。揭示网络的进化机制对于链接预测至关重要，如何有效利用历史信息来实现时间链接并有效提取网络结构的高阶模式仍然是一个至关重要的挑战。为了解决这些问题，在本文中，我们提出了一个具有调整后的Sigmoid函数和2-Simplex结构（TLPSS）的新型时间链接预测模型。调整后的Sigmoid衰减模式考虑了活跃，衰减和稳定的边缘状态，这适当适合信息的生命周期。此外，引入了由单纯形高阶结构组成的潜在矩阵序列，以增强链接预测方法的性能，因为它在稀疏网络中非常可行。结合信息的生命周期和单纯级结构，通过满足动态网络中时间和结构信息的一致性来实现TLPS的整体性能。六个现实世界数据集的实验结果证明了TLPS的有效性，与其他基线方法相比，我们提出的模型平均提高了链接预测的性能15％。

Most real-world networks suffer from incompleteness or incorrectness, which is an inherent attribute to real-world datasets. As a consequence, those downstream machine learning tasks in complex network like community detection methods may yield less satisfactory results, i.e., a proper preprocessing measure is required here. To address this issue, in this paper, we design a new community attribute based link prediction strategy HAP and propose a two-step community enhancement algorithm with automatic evolution process based on HAP. This paper aims at providing a community enhancement measure through adding links to clarify ambiguous community structures. The HAP method takes the neighbourhood uncertainty and Shannon entropy to identify boundary nodes, and establishes links by considering the nodes' community attributes and community size at the same time. The experimental results on twelve real-world datasets with ground truth community indicate that the proposed link prediction method outperforms other baseline methods and the enhancement of community follows the expected evolution process.

Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modelling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the loss of local details (e.g., boundaries and small objects), which are critical in medical image segmentation. To alleviate this problem, we propose a Dual-Aggregation Transformer Network called DuAT, which is characterized by two innovative designs, namely, the Global-to-Local Spatial Aggregation (GLSA) and Selective Boundary Aggregation (SBA) modules. The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively. The SBA module is used to aggregate the boundary characteristic from low-level features and semantic information from high-level features for better preserving boundary details and locating the re-calibration objects. Extensive experiments in six benchmark datasets demonstrate that our proposed model outperforms state-of-the-art methods in the segmentation of skin lesion images, and polyps in colonoscopy images. In addition, our approach is more robust than existing methods in various challenging situations such as small object segmentation and ambiguous object boundaries.

In this paper, we show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model dubbed ViTPose. Specifically, ViTPose employs the plain and non-hierarchical vision transformer as an encoder to encode features and a lightweight decoder to decode body keypoints in either a top-down or a bottom-up manner. It can be scaled up from about 20M to 1B parameters by taking advantage of the scalable model capacity and high parallelism of the vision transformer, setting a new Pareto front for throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, and pre-training and fine-tuning strategy. Based on the flexibility, a novel ViTPose+ model is proposed to deal with heterogeneous body keypoint categories in different types of body pose estimation tasks via knowledge factorization, i.e., adopting task-agnostic and task-specific feed-forward networks in the transformer. We also empirically demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Experimental results show that our ViTPose model outperforms representative methods on the challenging MS COCO Human Keypoint Detection benchmark at both top-down and bottom-up settings. Furthermore, our ViTPose+ model achieves state-of-the-art performance simultaneously on a series of body pose estimation tasks, including MS COCO, AI Challenger, OCHuman, MPII for human keypoint detection, COCO-Wholebody for whole-body keypoint detection, as well as AP-10K and APT-36K for animal keypoint detection, without sacrificing inference speed.

Self-supervised pre-training vision transformer (ViT) via masked image modeling (MIM) has been proven very effective. However, customized algorithms should be carefully designed for the hierarchical ViTs, e.g., GreenMIM, instead of using the vanilla and simple MAE for the plain ViT. More importantly, since these hierarchical ViTs cannot reuse the off-the-shelf pre-trained weights of the plain ViTs, the requirement of pre-training them leads to a massive amount of computational cost, thereby incurring both algorithmic and computational complexity. In this paper, we address this problem by proposing a novel idea of disentangling the hierarchical architecture design from the self-supervised pre-training. We transform the plain ViT into a hierarchical one with minimal changes. Technically, we change the stride of linear embedding layer from 16 to 4 and add convolution (or simple average) pooling layers between the transformer blocks, thereby reducing the feature size from 1/4 to 1/32 sequentially. Despite its simplicity, it outperforms the plain ViT baseline in classification, detection, and segmentation tasks on ImageNet, MS COCO, Cityscapes, and ADE20K benchmarks, respectively. We hope this preliminary study could draw more attention from the community on developing effective (hierarchical) ViTs while avoiding the pre-training cost by leveraging the off-the-shelf checkpoints. The code and models will be released at https://github.com/ViTAE-Transformer/HPViT.

With the advanced request to employ a team of robots to perform a task collaboratively, the research community has become increasingly interested in collaborative simultaneous localization and mapping. Unfortunately, existing datasets are limited in the scale and variation of the collaborative trajectories, even though generalization between inter-trajectories among different agents is crucial to the overall viability of collaborative tasks. To help align the research community's contributions with realistic multiagent ordinated SLAM problems, we propose S3E, a large-scale multimodal dataset captured by a fleet of unmanned ground vehicles along four designed collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor sequences that each exceed 200 seconds, consisting of well temporal synchronized and spatial calibrated high-frequency IMU, high-quality stereo camera, and 360 degree LiDAR data. Crucially, our effort exceeds previous attempts regarding dataset size, scene variability, and complexity. It has 4x as much average recording time as the pioneering EuRoC dataset. We also provide careful dataset analysis as well as baselines for collaborative SLAM and single counterparts. Data and more up-to-date details are found at https://github.com/PengYu-Team/S3E.

多模式变压器的最新努力通过合并视觉和文本信息改善了视觉上丰富的文档理解（VRDU）任务。但是，现有的方法主要集中于诸如单词和文档图像贴片之类的细粒元素，这使得他们很难从粗粒元素中学习，包括短语和显着视觉区域（如突出的图像区域）等自然词汇单元。在本文中，我们对包含高密度信息和一致语义的粗粒元素更为重要，这对于文档理解很有价值。首先，提出了文档图来模拟多层次多模式元素之间的复杂关系，其中通过基于群集的方法检测到显着的视觉区域。然后，提出了一种称为mmlayout的多模式变压器，以将粗粒的信息纳入基于图形的现有预训练的细颗粒的多峰变压器中。在mmlayout中，粗粒信息是从细粒度聚集的，然后在进一步处理后，将其融合到细粒度中以进行最终预测。此外，引入常识增强以利用天然词汇单元的语义信息。关于四个任务的实验结果，包括信息提取和文档问答，表明我们的方法可以根据细粒元素改善多模式变压器的性能，并使用更少的参数实现更好的性能。定性分析表明，我们的方法可以在粗粒元素中捕获一致的语义。