Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.
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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。
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Fine-grained visual recognition is to classify objects with visually similar appearances into subcategories, which has made great progress with the development of deep CNNs. However, handling subtle differences between different subcategories still remains a challenge. In this paper, we propose to solve this issue in one unified framework from two aspects, i.e., constructing feature-level interrelationships, and capturing part-level discriminative features. This framework, namely PArt-guided Relational Transformers (PART), is proposed to learn the discriminative part features with an automatic part discovery module, and to explore the intrinsic correlations with a feature transformation module by adapting the Transformer models from the field of natural language processing. The part discovery module efficiently discovers the discriminative regions which are highly-corresponded to the gradient descent procedure. Then the second feature transformation module builds correlations within the global embedding and multiple part embedding, enhancing spatial interactions among semantic pixels. Moreover, our proposed approach does not rely on additional part branches in the inference time and reaches state-of-the-art performance on 3 widely-used fine-grained object recognition benchmarks. Experimental results and explainable visualizations demonstrate the effectiveness of our proposed approach. The code can be found at https://github.com/iCVTEAM/PART.
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Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual special effects, just to name a few. Although deep learning-based human parsing solutions have made remarkable achievements, many important concepts, existing challenges, and potential research directions are still confusing. In this survey, we comprehensively review three core sub-tasks: single human parsing, multiple human parsing, and video human parsing, by introducing their respective task settings, background concepts, relevant problems and applications, representative literature, and datasets. We also present quantitative performance comparisons of the reviewed methods on benchmark datasets. Additionally, to promote sustainable development of the community, we put forward a transformer-based human parsing framework, providing a high-performance baseline for follow-up research through universal, concise, and extensible solutions. Finally, we point out a set of under-investigated open issues in this field and suggest new directions for future study. We also provide a regularly updated project page, to continuously track recent developments in this fast-advancing field: https://github.com/soeaver/awesome-human-parsing.
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视频分割,即将视频帧分组到多个段或对象中,在广泛的实际应用中扮演关键作用,例如电影中的视觉效果辅助,自主驾驶中的现场理解,以及视频会议中的虚拟背景创建,名称一些。最近,由于计算机愿景中的联系复兴,一直存在众多深度学习的方法,这一直专用于视频分割并提供引人注目的性能。在这项调查中,通过引入各自的任务设置,背景概念,感知需要,开发历史,以及开发历史,综合审查这一领域的两种基本研究,即在视频和视频语义分割中,即视频和视频语义分割中的通用对象分段(未知类别)。主要挑战。我们还提供关于两种方法和数据集的代表文学的详细概述。此外,我们在基准数据集中呈现了审查方法的定量性能比较。最后,我们指出了这一领域的一套未解决的开放问题,并提出了进一步研究的可能机会。
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深度学习的快速发展在分割方面取得了长足的进步,这是计算机视觉的基本任务之一。但是,当前的细分算法主要取决于像素级注释的可用性,这些注释通常昂贵,乏味且费力。为了减轻这一负担,过去几年见证了越来越多的关注,以建立标签高效,深度学习的细分算法。本文对标签有效的细分方法进行了全面的审查。为此,我们首先根据不同类型的弱标签提供的监督(包括没有监督,粗略监督,不完整的监督和嘈杂的监督和嘈杂的监督),首先开发出一种分类法来组织这些方法,并通过细分类型(包括语义细分)补充,实例分割和全景分割)。接下来,我们从统一的角度总结了现有的标签有效的细分方法,该方法讨论了一个重要的问题:如何弥合弱监督和密集预测之间的差距 - 当前的方法主要基于启发式先导,例如交叉像素相似性,跨标签约束,跨视图一致性,跨图像关系等。最后,我们分享了对标签有效深层细分的未来研究方向的看法。
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深度学习技术导致了通用对象检测领域的显着突破,近年来产生了很多场景理解的任务。由于其强大的语义表示和应用于场景理解,场景图一直是研究的焦点。场景图生成(SGG)是指自动将图像映射到语义结构场景图中的任务,这需要正确标记检测到的对象及其关系。虽然这是一项具有挑战性的任务,但社区已经提出了许多SGG方法并取得了良好的效果。在本文中,我们对深度学习技术带来了近期成就的全面调查。我们审查了138个代表作品,涵盖了不同的输入方式,并系统地将现有的基于图像的SGG方法从特征提取和融合的角度进行了综述。我们试图通过全面的方式对现有的视觉关系检测方法进行连接和系统化现有的视觉关系检测方法,概述和解释SGG的机制和策略。最后,我们通过深入讨论当前存在的问题和未来的研究方向来完成这项调查。本调查将帮助读者更好地了解当前的研究状况和想法。
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Image segmentation is a key topic in image processing and computer vision with applications such as scene understanding, medical image analysis, robotic perception, video surveillance, augmented reality, and image compression, among many others. Various algorithms for image segmentation have been developed in the literature. Recently, due to the success of deep learning models in a wide range of vision applications, there has been a substantial amount of works aimed at developing image segmentation approaches using deep learning models. In this survey, we provide a comprehensive review of the literature at the time of this writing, covering a broad spectrum of pioneering works for semantic and instance-level segmentation, including fully convolutional pixel-labeling networks, encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We investigate the similarity, strengths and challenges of these deep learning models, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.
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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.
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场景图是一个场景的结构化表示,可以清楚地表达场景中对象之间的对象,属性和关系。随着计算机视觉技术继续发展,只需检测和识别图像中的对象,人们不再满足。相反,人们期待着对视觉场景更高的理解和推理。例如,给定图像,我们希望不仅检测和识别图像中的对象,还要知道对象之间的关系(视觉关系检测),并基于图像内容生成文本描述(图像标题)。或者,我们可能希望机器告诉我们图像中的小女孩正在做什么(视觉问题应答(VQA)),甚至从图像中移除狗并找到类似的图像(图像编辑和检索)等。这些任务需要更高水平的图像视觉任务的理解和推理。场景图只是场景理解的强大工具。因此,场景图引起了大量研究人员的注意力,相关的研究往往是跨模型,复杂,快速发展的。然而,目前没有对场景图的相对系统的调查。为此,本调查对现行场景图研究进行了全面调查。更具体地说,我们首先总结了场景图的一般定义,随后对场景图(SGG)和SGG的发电方法进行了全面和系统的讨论,借助于先验知识。然后,我们调查了场景图的主要应用,并汇总了最常用的数据集。最后,我们对场景图的未来发展提供了一些见解。我们相信这将是未来研究场景图的一个非常有帮助的基础。
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现有的计算机视觉系统可以与人类竞争,以理解物体的可见部分,但在描绘部分被遮挡物体的无形部分时,仍然远远远远没有达到人类。图像Amodal的完成旨在使计算机具有类似人类的Amodal完成功能,以了解完整的对象,尽管该对象被部分遮住。这项调查的主要目的是对图像Amodal完成领域的研究热点,关键技术和未来趋势提供直观的理解。首先,我们对这个新兴领域的最新文献进行了全面的评论,探讨了图像Amodal完成中的三个关键任务,包括Amodal形状完成,Amodal外观完成和订单感知。然后,我们检查了与图像Amodal完成有关的流行数据集及其共同的数据收集方法和评估指标。最后,我们讨论了现实世界中的应用程序和未来的研究方向,以实现图像的完成,从而促进了读者对现有技术和即将到来的研究趋势的挑战的理解。
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Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.
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近年来,随着对公共安全的需求越来越多,智能监测网络的快速发展,人员重新识别(RE-ID)已成为计算机视野领域的热门研究主题之一。人员RE-ID的主要研究目标是从不同的摄像机中检索具有相同身份的人。但是,传统的人重新ID方法需要手动标记人的目标,这消耗了大量的劳动力成本。随着深度神经网络的广泛应用,出现了许多基于深入的基于学习的人物的方法。因此,本文促进研究人员了解最新的研究成果和该领域的未来趋势。首先,我们总结了对几个最近公布的人的研究重新ID调查,并补充了系统地分类基于深度学习的人的重新ID方法的最新研究方法。其次,我们提出了一种多维分类,根据度量标准和表示学习,将基于深度学习的人的重新ID方法分为四类,包括深度度量学习,本地特征学习,生成的对抗学习和序列特征学习的方法。此外,我们根据其方法和动机来细分以上四类,讨论部分子类别的优缺点。最后,我们讨论了一些挑战和可能的研究方向的人重新ID。
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Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for FOUR different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.
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即使在几个例子中,人类能够学会识别新物品。相比之下,培训基于深度学习的对象探测器需要大量的注释数据。为避免需求获取和注释这些大量数据,但很少拍摄的对象检测旨在从目标域中的新类别的少数对象实例中学习。在本调查中,我们在几次拍摄对象检测中概述了本领域的状态。我们根据培训方案和建筑布局分类方法。对于每种类型的方法,我们描述了一般的实现以及提高新型类别性能的概念。在适当的情况下,我们在这些概念上给出短暂的外卖,以突出最好的想法。最终,我们介绍了常用的数据集及其评估协议,并分析了报告的基准结果。因此,我们强调了评估中的共同挑战,并确定了这种新兴对象检测领域中最有前景的电流趋势。
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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。
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视觉表示学习在各种现实世界中无处不在,包括视觉理解,视频理解,多模式分析,人类计算机的互动和城市计算。由于出现了大量多模式的异质空间/时间/时空数据,因此在大数据时代,缺乏可解释性,鲁棒性和分布外的概括正在成为现有视觉模型的挑战。大多数现有方法倾向于符合原始数据/可变分布,而忽略了多模式知识背后的基本因果关系,该知识缺乏统一的指导和分析,并分析了为什么现代视觉表示学习方法很容易崩溃成数据偏见并具有有限的概括和认知能力。因此,受到人类水平代理人的强大推理能力的启发,近年来见证了巨大的努力,以发展因果推理范式,以良好的认知能力实现强大的代表性和模型学习。在本文中,我们对视觉表示学习的现有因果推理方法进行了全面审查,涵盖了基本理论,模型和数据集。还讨论了当前方法和数据集的局限性。此外,我们提出了一些预期的挑战,机会和未来的研究方向,用于基准视觉表示学习中的因果推理算法。本文旨在为这个新兴领域提供全面的概述,引起人们的注意,鼓励讨论,使发展新颖的因果推理方法,公开可用的基准和共识建设标准的紧迫性,以可靠的视觉表示和相关的真实实践。世界应用更有效。
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语义分割是图像的像素明智标记。由于在像素级别定义了问题,因此确定图像类标签是不可接受的,而是在原始图像像素分辨率下本地化它们是必要的。通过卷积神经网络(CNN)在创建语义,高级和分层图像特征方面的非凡能力推动;在过去十年中提出了几种基于深入的学习的2D语义分割方法。在本调查中,我们主要关注最近的语义细分科学发展,特别是在使用2D图像的基于深度学习的方法。我们开始分析了对2D语义分割的公共图像集和排行榜,概述了性能评估中使用的技术。在研究现场的演变时,我们按时间顺序分类为三个主要时期,即预先和早期的深度学习时代,完全卷积的时代和后FCN时代。我们在技术上分析了解决领域的基本问题的解决方案,例如细粒度的本地化和规模不变性。在借阅我们的结论之前,我们提出了一张来自所有提到的时代的方法表,每个方法都概述了他们对该领域的贡献。我们通过讨论现场当前的挑战以及他们已经解决的程度来结束调查。
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以前的人类解析模型仅限于将人类解析为预定义的类,这对于通常具有新时尚项目类的实用时尚应用是不灵活的。在本文中,我们定义了一个新颖的单次人类解析(OSHP)任务,该任务需要将人解析为任何测试示例定义的一组开放式类别。在培训期间,仅公开基础课程,这仅与一部分测试时间类别重叠。为了解决OSHP中的三个主要挑战,即小型,测试偏见和类似部分,我们设计了一个端到端的一击人类解析网络(EOP-NET)。首先,提出了一个端到端的人解析框架,以将查询图像解析为粗粒和细粒度的人类类别,该框架建立了一个强大的嵌入网络,具有在不同粒度上共享的丰富语义信息,从人类阶级。然后,我们通过逐步平滑训练时间静态原型来提出学习势头更新的原型,这有助于稳定训练并学习健壮的功能。此外,我们设计了一种双重度量学习方案,该方案鼓励网络增强特征的表示能力和可传递性。因此,我们的EOP-NET可以学习代表性功能,这些功能可以快速适应新颖的类并减轻测试偏置问题。此外,我们在原型水平上采用了对比损失,从而在细粒度度量空间中执行了类别之间的距离,以区分相似的部分。我们根据OSHP任务量身定制了三个现有的人类解析基准。新基准测试的实验表明,EOP-NET的表现优于大量边缘的代表性单次分割模型,这是进一步研究这项新任务的强大基线。源代码可从https://github.com/charleshhy/one-shot-human-parsing获得。
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Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works. We hope this effort will ignite further interest in the community to solve current challenges towards the application of transformer models in computer vision.
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