我们介绍了RP2K，这是一个新的大型零售产品数据集，用于细粒度图像分类。与以前的数据集专注于相对较少的产品，我们在归属于2000年不同产品的架子上收集超过500,000件零售产品图像。我们的数据集旨在推进零售对象识别的研究，该研究具有大量应用，如自动架审计和基于图像的产品信息检索。我们的数据集享受以下属性：（1）它是迄今为止产品类别的最大规模数据集。（2）所有图像在具有自然灯光的物理零售商店手动捕获，匹配真实应用的场景。（3）我们为每个对象提供丰富的注释，包括尺寸，形状和口味/气味。我们相信我们的数据集可以使计算机视觉研究和零售业受益。我们的数据集在https://www.pinlandata.com/rp2k_dataset上公开提供。

为了在商店中充分利用计算机视觉技术，需要考虑适合零售场景特征的实际需求。为了实现这一目标，我们介绍了联合零售数据集（Unitail），这是针对检测，阅读和匹配算法的产品的基本视觉任务的大规模基准。凭借注释的180万个四边形实例，该Unitail提供了一个检测数据集，以更好地对齐产品外观。此外，它提供了一个包含1454个产品类别，30k文本区域和21k转录的画廊风格的OCR数据集，以实现对产品的强大阅读并激励增强的产品匹配。除了使用各种最新技术对数据集进行基准测试外，我们还定制了一个新的检测器以进行产品检测，并提供了一个简单的基于OCR的匹配解决方案，以验证其有效性。

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals. It features visually similar species, captured in a wide variety of situations, from all over the world. Images were collected with different camera types, have varying image quality, feature a large class imbalance, and have been verified by multiple citizen scientists. We discuss the collection of the dataset and present extensive baseline experiments using state-of-the-art computer vision classification and detection models. Results show that current nonensemble based methods achieve only 67% top one classification accuracy, illustrating the difficulty of the dataset. Specifically, we observe poor results for classes with small numbers of training examples suggesting more attention is needed in low-shot learning.

尽管最先进的物体检测方法显示出令人信服的性能，但模型通常对对抗的攻击和分发数据不稳健。我们介绍了一个新的数据集，天然对手对象（Nao），以评估物体检测模型的稳健性。 Nao包含7,934个图像和9,943个对象，这些对象未经修改，代表了现实世界的情景，但导致最先进的检测模型以高信任误入歧途。与标准MSCOCO验证集相比，在NAO上评估时，高效的平均平均精度（MAP）降低74.5％。此外，通过比较各种对象检测架构，我们发现Mscoco验证集上的更好性能不一定转化为NAO的更好性能，这表明不能通过培训更准确的模型来简单地实现鲁棒性。我们进一步调查为什么NA​​O中难以检测和分类的原因。洗牌图像贴片的实验表明，模型对局部质地过于敏感。此外，使用集成梯度和背景替换，我们发现检测模型依赖于边界框内的像素信息，并且在预测类标签时对背景上下文不敏感。 Nao可以在https://drive.google.com/drive/folders/15p8sowojku6sseihlets86orfytgezi8下载。

Although Deep Neural Networks (DNNs) have achieved impressive results in computer vision, their exposed vulnerability to adversarial attacks remains a serious concern. A series of works has shown that by adding elaborate perturbations to images, DNNs could have catastrophic degradation in performance metrics. And this phenomenon does not only exist in the digital space but also in the physical space. Therefore, estimating the security of these DNNs-based systems is critical for safely deploying them in the real world, especially for security-critical applications, e.g., autonomous cars, video surveillance, and medical diagnosis. In this paper, we focus on physical adversarial attacks and provide a comprehensive survey of over 150 existing papers. We first clarify the concept of the physical adversarial attack and analyze its characteristics. Then, we define the adversarial medium, essential to perform attacks in the physical world. Next, we present the physical adversarial attack methods in task order: classification, detection, and re-identification, and introduce their performance in solving the trilemma: effectiveness, stealthiness, and robustness. In the end, we discuss the current challenges and potential future directions.

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.

Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of wellannotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect 2806 aerial images from different sensors and platforms. Each image is of the size about 4000 × 4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using 15 common object categories. The fully annotated DOTA images contains 188, 282 instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral. To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.

在计算机视觉中，构建实例检测模型并可以处理稀有对象类别是一个重要的挑战。但是，数据收集方法和指标缺乏对使用神经网络应用实际场景应用的研究。在这里，我们对对象遮挡数据收集和增强方法进行系统研究，在该方法中，我们模仿目标场景中的对象遮挡关系。但是，我们发现对象阻塞的简单机制足够好，并且可以在添加新类别的实际场景中提供可接受的准确性。我们幻想的是，只有数百个类别的50万培训数据集中添加15张新类别的图像，可以在看不见的测试数据集中提供这个新类别95％的准确性，其中包括数千张此类别的图像。

由于其前所未有的优势，在规模，移动，部署和隐蔽观察能力方面，空中平台和成像传感器的快速出现是实现新的空中监测形式。本文从计算机视觉和模式识别的角度来看，全面概述了以人为本的空中监控任务。它旨在为读者提供使用无人机，无人机和其他空中平台的空中监测任务当前状态的深入系统审查和技术分析。感兴趣的主要对象是人类，其中要检测单个或多个受试者，识别，跟踪，重新识别并进行其行为。更具体地，对于这四项任务中的每一个，我们首先讨论与基于地面的设置相比在空中环境中执行这些任务的独特挑战。然后，我们审查和分析公共可用于每项任务的航空数据集，并深入了解航空文学中的方法，并调查他们目前如何应对鸟瞰挑战。我们在讨论缺失差距和开放研究问题的讨论中得出结论，告知未来的研究途径。

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the chal-

This paper presents a new large scale multi-person tracking dataset -- \texttt{PersonPath22}, which is over an order of magnitude larger than currently available high quality multi-object tracking datasets such as MOT17, HiEve, and MOT20 datasets. The lack of large scale training and test data for this task has limited the community's ability to understand the performance of their tracking systems on a wide range of scenarios and conditions such as variations in person density, actions being performed, weather, and time of day. \texttt{PersonPath22} dataset was specifically sourced to provide a wide variety of these conditions and our annotations include rich meta-data such that the performance of a tracker can be evaluated along these different dimensions. The lack of training data has also limited the ability to perform end-to-end training of tracking systems. As such, the highest performing tracking systems all rely on strong detectors trained on external image datasets. We hope that the release of this dataset will enable new lines of research that take advantage of large scale video based training data.

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.

随着深度卷积神经网络的兴起，对象检测在过去几年中取得了突出的进步。但是，这种繁荣无法掩盖小物体检测（SOD）的不令人满意的情况，这是计算机视觉中臭名昭著的挑战性任务之一，这是由于视觉外观不佳和由小目标的内在结构引起的嘈杂表示。此外，用于基准小对象检测方法基准测试的大规模数据集仍然是瓶颈。在本文中，我们首先对小物体检测进行了详尽的审查。然后，为了催化SOD的发展，我们分别构建了两个大规模的小物体检测数据集（SODA），SODA-D和SODA-A，分别集中在驾驶和空中场景上。 SODA-D包括24704个高质量的交通图像和277596个9个类别的实例。对于苏打水，我们收集2510个高分辨率航空图像，并在9个类别上注释800203实例。众所周知，拟议的数据集是有史以来首次尝试使用针对多类SOD量身定制的大量注释实例进行大规模基准测试。最后，我们评估主流方法在苏打水上的性能。我们预计发布的基准可以促进SOD的发展，并产生该领域的更多突破。数据集和代码将很快在：\ url {https://shaunyuan22.github.io/soda}上。

It is desirable for detection and classification algorithms to generalize to unfamiliar environments, but suitable benchmarks for quantitatively studying this phenomenon are not yet available. We present a dataset designed to measure recognition generalization to novel environments. The images in our dataset are harvested from twenty camera traps deployed to monitor animal populations. Camera traps are fixed at one location, hence the background changes little across images; capture is triggered automatically, hence there is no human bias. The challenge is learning recognition in a handful of locations, and generalizing animal detection and classification to new locations where no training data is available. In our experiments state-of-the-art algorithms show excellent performance when tested at the same location where they were trained. However, we find that generalization to new locations is poor, especially for classification systems.

多年来，为各种对象检测任务开发了数据集。海事域中的对象检测对于船舶的安全和导航至关重要。但是，在海事域中，仍然缺乏公开可用的大规模数据集。为了克服这一挑战，我们提出了Kolomverse，这是一个开放的大型图像数据集，可在Kriso（韩国研究所和海洋工程研究所）的海事域中进行物体检测。我们收集了从韩国21个领土水域捕获的5,845小时的视频数据。通过精心设计的数据质量评估过程，我们从视频数据中收集了大约2,151,470 4K分辨率的图像。该数据集考虑了各种环境：天气，时间，照明，遮挡，观点，背景，风速和可见性。 Kolomverse由五个类（船，浮标，渔网浮标，灯塔和风电场）组成，用于海上对象检测。该数据集的图像为3840美元$ \ times $ 2160像素，据我们所知，它是迄今为止最大的公开数据集，用于海上域中的对象检测。我们进行了对象检测实验，并在几个预训练的最先进的架构上评估了我们的数据集，以显示我们数据集的有效性和实用性。该数据集可在：\ url {https://github.com/maritimedataset/kolomverse}中获得。

细粒度的图像分析（FGIA）是计算机视觉和模式识别中的长期和基本问题，并为一组多种现实世界应用提供了基础。 FGIA的任务是从属类别分析视觉物体，例如汽车或汽车型号的种类。细粒度分析中固有的小阶级和阶级阶级内变异使其成为一个具有挑战性的问题。利用深度学习的进步，近年来，我们在深入学习动力的FGIA中见证了显着进展。在本文中，我们对这些进展的系统进行了系统的调查，我们试图通过巩固两个基本的细粒度研究领域 - 细粒度的图像识别和细粒度的图像检索来重新定义和扩大FGIA领域。此外，我们还审查了FGIA的其他关键问题，例如公开可用的基准数据集和相关域的特定于应用程序。我们通过突出几个研究方向和开放问题，从社区中突出了几个研究方向和开放问题。

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.

在过去的十年中，深度学习急剧改变了传统的手工艺特征方式，具有强大的功能学习能力，从而极大地改善了传统任务。然而，最近已经证明了深层神经网络容易受到对抗性例子的影响，这种恶意样本由小型设计的噪音制作，误导了DNNs做出错误的决定，同时仍然对人类无法察觉。对抗性示例可以分为数字对抗攻击和物理对抗攻击。数字对抗攻击主要是在实验室环境中进行的，重点是改善对抗性攻击算法的性能。相比之下，物理对抗性攻击集中于攻击物理世界部署的DNN系统，这是由于复杂的物理环境（即亮度，遮挡等），这是一项更具挑战性的任务。尽管数字对抗和物理对抗性示例之间的差异很小，但物理对抗示例具有特定的设计，可以克服复杂的物理环境的效果。在本文中，我们回顾了基于DNN的计算机视觉任务任务中的物理对抗攻击的开发，包括图像识别任务，对象检测任务和语义细分。为了完整的算法演化，我们将简要介绍不涉及身体对抗性攻击的作品。我们首先提出一个分类方案，以总结当前的物理对抗攻击。然后讨论现有的物理对抗攻击的优势和缺点，并专注于用于维持对抗性的技术，当应用于物理环境中时。最后，我们指出要解决的当前身体对抗攻击的问题并提供有前途的研究方向。

弱监督的对象本地化（WSOL）旨在学习仅使用图像级类别标签编码对象位置的表示形式。但是，许多物体可以在不同水平的粒度标记。它是动物，鸟还是大角的猫头鹰？我们应该使用哪些图像级标签？在本文中，我们研究了标签粒度在WSOL中的作用。为了促进这项调查，我们引入了Inatloc500，这是一个新的用于WSOL的大规模细粒基准数据集。令人惊讶的是，我们发现选择正确的训练标签粒度比选择最佳的WSOL算法提供了更大的性能。我们还表明，更改标签粒度可以显着提高数据效率。

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.