在临床环境中，通过视频脑电图（EEG）测试监测癫痫患者。视频EEG记录eEG设备记录其脑波时录像带的患者体验。目前，在癫痫发作期间，没有现有的自动化方法用于跟踪患者位置，以及医院患者的视频录制与公开的视频基准数据集大致不同。例如，摄像机角度可能是不寻常的，患者可以部分地覆盖有床上用品和电极组。能够与视频EEG实时跟踪患者将是提高医疗保健质量的有希望的创新。具体而言，自动患者检测系统可以补充临床监督，并降低需要连续监测患者的护士和医生的资源密集努力。我们评估了一个想象的预先训练的面罩R-CNN，一种标准的对象检测深度学习模型，用于使用我们自己的45岁患者45个视频的策划数据集的患者检测任务。数据集被聚合并策划此工作。我们展示没有微调的情况下，Imagenet预训练的掩模R-CNN模型在这些数据上表现不佳。通过微调具有我们数据集的子集的模型，我们观察患者检测性能的大量改善，平均平均精度为0.64。我们表明结果基本上取决于视频剪辑。

In this paper we present a new computer vision task, named video instance segmentation. The goal of this new task is simultaneous detection, segmentation and tracking of instances in videos. In words, it is the first time that the image instance segmentation problem is extended to the video domain. To facilitate research on this new task, we propose a large-scale benchmark called YouTube-VIS, which consists of 2,883 high-resolution YouTube videos, a 40-category label set and 131k high-quality instance masks.In addition, we propose a novel algorithm called Mask-Track R-CNN for this task. Our new method introduces a new tracking branch to Mask R-CNN to jointly perform the detection, segmentation and tracking tasks simultaneously. Finally, we evaluate the proposed method and several strong baselines on our new dataset. Experimental results clearly demonstrate the advantages of the proposed algorithm and reveal insight for future improvement. We believe the video instance segmentation task will motivate the community along the line of research for video understanding.

在本文中，我们介绍了Siammask，这是一个实时使用相同简单方法实时执行视觉对象跟踪和视频对象分割的框架。我们通过通过二进制细分任务来增强其损失，从而改善了流行的全面暹罗方法的离线培训程序。离线训练完成后，SiamMask只需要一个单个边界框来初始化，并且可以同时在高框架速率下进行视觉对象跟踪和分割。此外，我们表明可以通过简单地以级联的方式重新使用多任务模型来扩展框架以处理多个对象跟踪和细分。实验结果表明，我们的方法具有较高的处理效率，每秒约55帧。它可以在视觉对象跟踪基准测试中产生实时最新结果，同时以高速进行视频对象分割基准测试以高速显示竞争性能。

The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10× or 100×? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between 'enormous data' and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pretraining) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-theart results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets.

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.

Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.

In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbed SiamMask, improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task. Once trained, SiamMask solely relies on a single bounding box initialisation and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second. Despite its simplicity, versatility and fast speed, our strategy allows us to establish a new state of the art among real-time trackers on VOT-2018, while at the same time demonstrating competitive performance and the best speed for the semisupervised video object segmentation task on DAVIS-2016 and DAVIS-2017. The project website is http://www. robots.ox.ac.uk/ ˜qwang/SiamMask.

Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe and efficient surgery. Feedback from expert surgeons, while especially valuable in this regard, is challenging to acquire due to their typically busy schedules, and may be subject to biases. Formal assessment procedures like OSATS and GEARS attempt to provide objective measures of skill, but remain time-consuming. With advances in machine learning there is an opportunity for fast and objective automated feedback on technical skills. The SimSurgSkill 2021 challenge (hosted as a sub-challenge of EndoVis at MICCAI 2021) aimed to promote and foster work in this endeavor. Using virtual reality (VR) surgical tasks, competitors were tasked with localizing instruments and predicting surgical skill. Here we summarize the winning approaches and how they performed. Using this publicly available dataset and results as a springboard, future work may enable more efficient training of surgeons with advances in surgical data science. The dataset can be accessed from https://console.cloud.google.com/storage/browser/isi-simsurgskill-2021.

We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance.Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012-achieving a mAP of 53.3%. Our approach combines two key insights:(1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also compare R-CNN to OverFeat, a recently proposed sliding-window detector based on a similar CNN architecture. We find that R-CNN outperforms OverFeat by a large margin on the 200-class ILSVRC2013 detection dataset. Source code for the complete system is available at http://www.cs.berkeley.edu/ ˜rbg/rcnn.

开放程序代表全球手术的主要形式。人工智能（AI）有可能优化手术实践并改善患者结果，但努力主要集中在微创技术上。我们的工作通过策划，从YouTube，从YouTube，Open Surgical视频的最大数据集克服了培训AI模型的现有数据限制：1997年从50个国家上传的23个外科手术的视频。使用此数据集，我们开发了一种能够实时了解外科行为，手和工具的多任务AI模型 - 程序流程和外科医生技能的构建块。我们表明我们的模型推广了各种外科类型和环境。说明这种普遍性，我们直接应用了YouTube培训的模型，分析了在学术医疗中心前瞻性收集的开放式手术，并确定了与手动效率相关的外科技能的运动学描述符。我们的开放外科（AVOS）数据集和培训模式的注释视频将可用于进一步发展外科艾。

当前的时空动作管检测方法通常将一个给定键框的边界框提案扩展到附近帧的3D颞轴和池特征。但是，如果演员的位置或形状通过大型的2D运动和可变性，由于大型摄像机运动，大型演员形状变形，快速演员的动作等，这种合并就无法积累有意义的时空特征。在这项工作中，我们旨在研究在大动作下的动作检测中观察到Cuboid感知特征聚集的性能。此外，我们建议通过跟踪参与者并沿各个轨道进行时间特征聚集来增强演员特征表示。我们在各种固定时间尺度的动作管/轨道框之间使用相交的行动者（IOU）定义了演员运动。随着时间的推移，具有较大运动的动作将导致较低的IOU，并且较慢的动作将保持更高的IOU。我们发现，轨道感知功能聚集始终取得了巨大的改善，尤其是对于与Cuboid感知的基线相比，在大型运动下进行的动作。结果，我们还报告了大规模多运动数据集的最先进。

虽然深度学习方法近年来取得了高级视频对象识别性能，但在视频中感知封闭对象仍然是一个非常具有挑战性的任务。为促进遮挡理解的发展，我们在遮挡方案中收集一个名为OVIS的大规模数据集，用于遮挡方案中的视频实例分段。 ovis由296K高质量的屏幕和901个遮挡场景组成。虽然我们的人类视觉系统可以通过语境推理和关联来感知那些遮挡物体，但我们的实验表明当前的视频了解系统不能。在ovis数据集上，所有基线方法都遇到了大约80％的大约80％的大约80％，这表明仍然有很长的路要走在复杂的真实情景中理解模糊物体和视频。为了促进对视频理解系统的新范式研究，我们基于OVI数据集启动了挑战。提交的顶级执行算法已经比我们的基线实现了更高的性能。在本文中，我们将介绍OVIS数据集，并通过分析基线的结果和提交的方法来进一步剖析。可以在http://songbai.site/ovis找到ovis数据集和挑战信息。

基于模板的鉴别性跟踪器是目前主导的跟踪范例由于其稳健性，但不限于边界框跟踪和有限的转换模型，这降低了它们的本地化准确性。我们提出了一个判别的单次分割跟踪器 - D3S2，其缩小了视觉对象跟踪和视频对象分段之间的差距。单次网络应用两个具有互补的几何属性的目标模型，一个不变的变换，包括非刚性变形，另一个假设刚性对象同时实现强大的在线目标分段。通过解耦对象和特征比例估计，进一步提高了整体跟踪可靠性。没有每数据集FineTuning，并且仅用于分段作为主要输出，D3S2胜过最近的短期跟踪基准Vot2020上的所有已发布的跟踪器，并非常接近GOT-10K上的最先进的跟踪器， TrackingNet，OTB100和Lasot。 D3S2优于视频对象分段基准上的前导分割跟踪器SIAMMASK，并与顶部视频对象分段算法进行操作。

如今，使用微创手术（MIS）进行了更多的手术程序。这是由于其许多好处，例如最小的术后问题，较少的出血，较小的疤痕和快速的康复。但是，MIS的视野，小手术室和对操作场景的间接查看可能导致手术工具发生冲突并可能损害人体器官或组织。因此，通过使用内窥镜视频饲料实时检测和监视手术仪器，可以大大减少MIS问题，并且可以提高手术程序的准确性和成功率。在本文中，研究，分析和评估了对Yolov5对象检测器的一系列改进，以增强手术仪器的检测。在此过程中，我们进行了基于性能的消融研究，探索了改变Yolov5模型的骨干，颈部和锚固结构元素的影响，并注释了独特的内窥镜数据集。此外，我们将消融研究的有效性与其他四个SOTA对象探测器（Yolov7，Yolor，Scaled-Yolov4和Yolov3-SPP）进行了比较。除了Yolov3-SPP（在MAP中具有98.3％的模型性能和相似的推理速度）外，我们的所有基准模型（包括原始的Yolov5）在使用新的内窥镜数据集的实验中超过了我们的顶级精制模型。

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without tricks, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code will be made available.

Recent leading approaches to semantic segmentation rely on deep convolutional networks trained with humanannotated, pixel-level segmentation masks. Such pixelaccurate supervision demands expensive labeling effort and limits the performance of deep networks that usually benefit from more training data. In this paper, we propose a method that achieves competitive accuracy but only requires easily obtained bounding box annotations. The basic idea is to iterate between automatically generating region proposals and training convolutional networks. These two steps gradually recover segmentation masks for improving the networks, and vise versa. Our method, called "BoxSup", produces competitive results (e.g., 62.0% mAP for validation) supervised by boxes only, on par with strong baselines (e.g., 63.8% mAP) fully supervised by masks under the same setting. By leveraging a large amount of bounding boxes, BoxSup further unleashes the power of deep convolutional networks and yields state-of-the-art results on PAS-CAL VOC 2012 and PASCAL-CONTEXT [24].

对人类对象相互作用的理解在第一人称愿景（FPV）中至关重要。遵循相机佩戴者操纵的对象的视觉跟踪算法可以提供有效的信息，以有效地建模此类相互作用。在过去的几年中，计算机视觉社区已大大提高了各种目标对象和场景的跟踪算法的性能。尽管以前有几次尝试在FPV域中利用跟踪器，但仍缺少对最先进跟踪器的性能的有条理分析。这项研究差距提出了一个问题，即应使用当前的解决方案``现成''还是应进行更多特定领域的研究。本文旨在为此类问题提供答案。我们介绍了FPV中单个对象跟踪的首次系统研究。我们的研究广泛分析了42个算法的性能，包括通用对象跟踪器和基线FPV特定跟踪器。分析是通过关注FPV设置的不同方面，引入新的绩效指标以及与FPV特定任务有关的。这项研究是通过引入Trek-150（由150个密集注释的视频序列组成的新型基准数据集）来实现的。我们的结果表明，FPV中的对象跟踪对当前的视觉跟踪器构成了新的挑战。我们强调了导致这种行为的因素，并指出了可能的研究方向。尽管遇到了困难，但我们证明了跟踪器为需要短期对象跟踪的FPV下游任务带来好处。我们预计，随着新的和FPV特定的方法学会得到研究，通用对象跟踪将在FPV中受欢迎。

在本文中，我们提出了区块拷贝，该方案与标准的逐帧处理相比，可以加速基于框架的CNN以更有效地处理视频。为此，轻巧的策略网络确定图像中的重要区域，并且仅使用自定义的块 - 帕斯斯卷积应用于选定区域。简单地从前一个帧复制了非选择区域的特征，从而减少了计算和延迟的数量。执行策略是通过在线方式使用强化学习培训的，而无需进行地面真相注释。我们的通用框架在密集的预测任务上进行了证明，例如人行人检测，实例分割和语义分割，同时使用最新技术（中心和比例预测指标，MGAN，MGAN，SWIFTNET）和标准基线网络（Mask-RCNN，DeepLabV3+）。区块拷贝可实现大量的拖放节省和推理速度，对准确性的影响最小。

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K 1 , the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.