在严重犯罪的情况下,包括性虐待,往往是唯一可以证明识别潜力的可用信息是手的图像。由于这种证据在不受控制的情况下捕获,因此难以分析。随着全局对特征比较的方法在这种情况下有限,重要的是要考虑当地信息。在这项工作中,我们通过学习全球和地方深度特征表示来提出基于手的人识别。我们提出的方法,全局和部分感知网络(GPA-Net),在Conv-Tother上创建全局和本地分支,以学习强大的歧视全局和零级功能。为了学习本地(零件级别)功能,我们在水平和垂直方向上对CONC层执行统一分区。我们通过进行软分区检索零件,而无需明确地分区图像或需要外部提示,例如姿势估计。我们对两个大型多民族和公开的手部数据集进行了广泛的评估,表明我们所提出的方法显着优于竞争方法。
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在本文中,我们提出了一种新的手工识别方法,以便犯罪调查,因为手形象往往是在严重犯罪如性虐待中的唯一可用信息。我们提出的方法,使用注意网络(MBA-Net)多分支,除了全球(不受注意)分支之外,还包含了分支机构中的通道和空间注意模块,以捕获歧视特征学习的全局结构信息。注意力模块侧重于手形图像的相关特征,同时抑制无关背景。为了克服注意力机制的弱点,等离性体到像素混洗,我们将相对位置编码集成到空间注意模块中以捕获像素的空间位置。对两个大型多民族和公共手部数据集进行广泛的评估表明,我们的提出方法实现了最先进的性能,超越了现有的基于手的识别方法。
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从图像中学习代表,健壮和歧视性信息对于有效的人重新识别(RE-ID)至关重要。在本文中,我们提出了一种基于身体和手部图像的人重新ID的端到端判别深度学习的复合方法。我们仔细设计了本地感知的全球注意力网络(Laga-Net),这是一个多分支深度网络架构,由一个用于空间注意力的分支组成,一个用于渠道注意。注意分支集中在图像的相关特征上,同时抑制了无关紧要的背景。为了克服注意力机制的弱点,与像素改组一样,我们将相对位置编码整合到空间注意模块中以捕获像素的空间位置。全球分支机构打算保留全球环境或结构信息。对于打算捕获细粒度信息的本地分支,我们进行统一的分区以水平在Conv-Layer上生成条纹。我们通过执行软分区来检索零件,而无需明确分区图像或需要外部线索,例如姿势估计。一组消融研究表明,每个组件都会有助于提高拉加网络的性能。对四个受欢迎的人体重新ID基准和两个公开可用的手数据集的广泛评估表明,我们的建议方法始终优于现有的最新方法。
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Employing part-level features for pedestrian image description offers fine-grained information and has been verified as beneficial for person retrieval in very recent literature. A prerequisite of part discovery is that each part should be well located. Instead of using external cues, e.g., pose estimation, to directly locate parts, this paper lays emphasis on the content consistency within each part.Specifically, we target at learning discriminative partinformed features for person retrieval and make two contributions. (i) A network named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. With a uniform partition strategy, PCB achieves competitive results with the state-of-the-art methods, proving itself as a strong convolutional baseline for person retrieval. (ii) A refined part pooling (RPP) method. Uniform partition inevitably incurs outliers in each part, which are in fact more similar to other parts. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)% rank-1 accuracy, surpassing the state of the art by a large margin.
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The combination of global and partial features has been an essential solution to improve discriminative performances in person re-identification (Re-ID) tasks. Previous part-based methods mainly focus on locating regions with specific pre-defined semantics to learn local representations, which increases learning difficulty but not efficient or robust to scenarios with large variances. In this paper, we propose an end-to-end feature learning strategy integrating discriminative information with various granularities. We carefully design the Multiple Granularity Network (MGN), a multi-branch deep network architecture consisting of one branch for global feature representations and two branches for local feature representations. Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities. Comprehensive experiments implemented on the mainstream evaluation datasets including Market-1501, DukeMTMC-reid and CUHK03 indicate that our method robustly achieves state-of-the-art performances and outperforms any existing approaches by a large margin. For example, on Market-1501 dataset in single query mode, we obtain a top result of Rank-1/mAP=96.6%/94.2% with this method after re-ranking.
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近年来,围面识别被制定为有价值的生物识别方法,特别是在野生环境中(例如,由于Covid-19大流行导致的遮阳面),其中面部识别可能不适用。本文提出了一种名为基于属性的深周相识别(ADPR)的新的深周围识别框架,其预测软生物学测量,并将预测结合到周边识别算法中,以确定具有高精度的围绕围绕围绕图像的标识。我们提出了一个端到端的框架,它使用了几个共享卷积神经网络(CNN)层(公共网络),其输出馈送两个单独的专用分支(模态专用层);第一分支在第二分支预测软管生物识别技术的同时分类周边图像。接下来,来自这两个分支的特征融合在一起以获得最终的周边识别。所提出的方法与现有方法不同,因为它不仅使用共享的CNN特征空间来共同培训这两个任务,而且还融合了预测的软生物识别功能,具有训练步骤中的周边特征,以提高整体周边识别性能。我们的建议模型使用四个不同的公共数据集进行了广泛的评估。实验结果表明,基于软生物识别的外观识别方法优于野生环境中的其他最先进的方法。
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人重新识别(Reid)旨在从不同摄像机捕获的图像中检索一个人。对于基于深度学习的REID方法,已经证明,使用本地特征与人物图像的全局特征可以帮助为人员检索提供强大的特征表示。人类的姿势信息可以提供人体骨架的位置,有效地指导网络在这些关键领域更加关注这些关键领域,也可能有助于减少来自背景或闭塞的噪音分散。然而,先前与姿势相关的作品提出的方法可能无法充分利用姿势信息的好处,并没有考虑不同当地特征的不同贡献。在本文中,我们提出了一种姿势引导图注意网络,一个多分支架构,包括一个用于全局特征的一个分支,一个用于中粒体特征的一个分支,一个分支用于细粒度关键点特征。我们使用预先训练的姿势估计器来生成本地特征学习的关键点热图,并仔细设计图表卷积层以通过建模相似关系来重新评估提取的本地特征的贡献权重。实验结果表明我们对歧视特征学习的方法的有效性,我们表明我们的模型在几个主流评估数据集上实现了最先进的表演。我们还对我们的网络进行了大量的消融研究和设计不同类型的比较实验,以证明其有效性和鲁棒性,包括整体数据集,部分数据集,遮挡数据集和跨域测试。
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可见红外人重新识别(VI-REID)由于可见和红外模式之间存在较大的差异而受到挑战。大多数开创性方法通过学习模态共享和ID相关的功能来降低类内变型和跨性间差异。但是,在VI-REID中尚未充分利用一个显式模态共享提示。此外,现有特征学习范例在全局特征或分区特征条带上强加约束,忽略了全局和零件特征的预测一致性。为了解决上述问题,我们将构成估算作为辅助学习任务,以帮助vi-reid任务在端到端的框架中。通过以互利的方式联合培训这两个任务,我们的模型学习了更高质量的模态共享和ID相关的功能。在它之上,通过分层特征约束(HFC)无缝同步全局功能和本地特征的学习,前者使用知识蒸馏策略监督后者。两个基准VI-REID数据集的实验结果表明,该方法始终如一地通过显着的利润来改善最先进的方法。具体而言,我们的方法在RegDB数据集上取决于针对最先进的方法的近20美元\%$地图改进。我们的兴趣调查结果突出了vi-reid中辅助任务学习的使用。
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最近,无监督的人重新识别(RE-ID)引起了人们的关注,因为其开放世界情景设置有限,可用的带注释的数据有限。现有的监督方法通常无法很好地概括在看不见的域上,而无监督的方法(大多数缺乏多范围的信息),并且容易患有确认偏见。在本文中,我们旨在从两个方面从看不见的目标域上找到更好的特征表示形式,1)在标记的源域上进行无监督的域适应性和2)2)在未标记的目标域上挖掘潜在的相似性。此外,提出了一种协作伪标记策略,以减轻确认偏见的影响。首先,使用生成对抗网络将图像从源域转移到目标域。此外,引入了人身份和身份映射损失,以提高生成图像的质量。其次,我们提出了一个新颖的协作多元特征聚类框架(CMFC),以学习目标域的内部数据结构,包括全局特征和部分特征分支。全球特征分支(GB)在人体图像的全球特征上采用了无监督的聚类,而部分特征分支(PB)矿山在不同人体区域内的相似性。最后,在两个基准数据集上进行的广泛实验表明,在无监督的人重新设置下,我们的方法的竞争性能。
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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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This paper explores a simple and efficient baseline for person re-identification (ReID). Person re-identification (ReID) with deep neural networks has made progress and achieved high performance in recent years. However, many state-of-the-arts methods design complex network structure and concatenate multi-branch features. In the literature, some effective training tricks are briefly appeared in several papers or source codes. This paper will collect and evaluate these effective training tricks in person ReID. By combining these tricks together, the model achieves 94.5% rank-1 and 85.9% mAP on Market1501 with only using global features. Our codes and models are available at https://github.com/michuanhaohao/reid-strong-baseline * Equal contributions. This work was partially done when Hao Luo and Xingyu Liao were interns at Megvii Inc.
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Existing person re-identification (re-id) methods either assume the availability of well-aligned person bounding box images as model input or rely on constrained attention selection mechanisms to calibrate misaligned images. They are therefore sub-optimal for re-id matching in arbitrarily aligned person images potentially with large human pose variations and unconstrained auto-detection errors. In this work, we show the advantages of jointly learning attention selection and feature representation in a Convolutional Neural Network (CNN) by maximising the complementary information of different levels of visual attention subject to re-id discriminative learning constraints. Specifically, we formulate a novel Harmonious Attention CNN (HA-CNN) model for joint learning of soft pixel attention and hard regional attention along with simultaneous optimisation of feature representations, dedicated to optimise person re-id in uncontrolled (misaligned) images. Extensive comparative evaluations validate the superiority of this new HA-CNN model for person re-id over a wide variety of state-ofthe-art methods on three large-scale benchmarks including CUHK03, Market-1501, and DukeMTMC-ReID.
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学习模态不变功能是可见热跨模板人员重新凝视(VT-REID)问题的核心,其中查询和画廊图像来自不同的模式。现有工作通过使用对抗性学习或仔细设计特征提取模块来隐式地将像素和特征空间中的模态对齐。我们提出了一个简单但有效的框架MMD-REID,通过明确的差异减少约束来降低模态差距。 MMD-REID从最大均值(MMD)中获取灵感,广泛使用的统计工具用于确定两个分布之间的距离。 MMD-REID采用新的基于边缘的配方,以匹配可见和热样品的类条件特征分布,以最大限度地减少级别的距离,同时保持特征辨别性。 MMD-Reid是一个简单的架构和损失制定方面的框架。我们对MMD-REID的有效性进行了广泛的实验,以使MMD-REID对调整边缘和阶级条件分布的有效性,从而学习模型无关和身份的一致特征。所提出的框架显着优于Sysu-MM01和RegDB数据集的最先进的方法。代码将在https://github.com/vcl-iisc/mmd -reid发布
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Person re-identification is a challenging task because of the high intra-class variance induced by the unrestricted nuisance factors of variations such as pose, illumination, viewpoint, background, and sensor noise. Recent approaches postulate that powerful architectures have the capacity to learn feature representations invariant to nuisance factors, by training them with losses that minimize intra-class variance and maximize inter-class separation, without modeling nuisance factors explicitly. The dominant approaches use either a discriminative loss with margin, like the softmax loss with the additive angular margin, or a metric learning loss, like the triplet loss with batch hard mining of triplets. Since the softmax imposes feature normalization, it limits the gradient flow supervising the feature embedding. We address this by joining the losses and leveraging the triplet loss as a proxy for the missing gradients. We further improve invariance to nuisance factors by adding the discriminative task of predicting attributes. Our extensive evaluation highlights that when only a holistic representation is learned, we consistently outperform the state-of-the-art on the three most challenging datasets. Such representations are easier to deploy in practical systems. Finally, we found that joining the losses removes the requirement for having a margin in the softmax loss while increasing performance.
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Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. For addressing occluded ReID, part-based methods have been shown beneficial as they offer fine-grained information and are well suited to represent partially visible human bodies. However, training a part-based model is a challenging task for two reasons. Firstly, individual body part appearance is not as discriminative as global appearance (two distinct IDs might have the same local appearance), this means standard ReID training objectives using identity labels are not adapted to local feature learning. Secondly, ReID datasets are not provided with human topographical annotations. In this work, we propose BPBreID, a body part-based ReID model for solving the above issues. We first design two modules for predicting body part attention maps and producing body part-based features of the ReID target. We then propose GiLt, a novel training scheme for learning part-based representations that is robust to occlusions and non-discriminative local appearance. Extensive experiments on popular holistic and occluded datasets show the effectiveness of our proposed method, which outperforms state-of-the-art methods by 0.7% mAP and 5.6% rank-1 accuracy on the challenging Occluded-Duke dataset. Our code is available at https://github.com/VlSomers/bpbreid.
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改变布料的人重新识别(REID)是一个新出现的研究主题,旨在检索换衣服的行人。由于带有不同衣服的人类外观表现出较大的变化,因此现有方法很难提取歧视性和健壮的特征表示。当前的作品主要集中在身体形状或轮廓草图上,但是人类的语义信息以及换衣服之前和之后的行人特征的潜在一致性未被充分探索或被忽略。为了解决这些问题,在这项工作中,提出了一种新颖的语义意识到的注意力和视觉屏蔽网络,用于换衣服的人Reid(缩写为SAV),其中关键的想法是屏蔽与衣服外观相关的线索,只关注衣服的外观对视图/姿势变化不敏感的视觉语义信息。具体而言,首先采用了视觉语义编码器来基于人类语义分割信息来定位人体和服装区域。然后,提出了人类的语义注意模块(HSA),以突出显示人类的语义信息并重新授予视觉特征图。此外,视觉服装屏蔽模块(VCS)还旨在通过覆盖衣服区域并将模型集中在与衣服无关的视觉语义信息上来提取更健壮的特征代表。最重要的是,这两个模块在端到端统一框架中共同探索。广泛的实验表明,所提出的方法可以显着胜过最先进的方法,并且可以为换衣的人提取更健壮的特征。与FSAM(在CVPR 2021中发布)相比,该方法可以分别在LTCC和PRCC数据集上以MAP(RANK-1)的形式获得32.7%(16.5%)和14.9%( - )。
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无监督的视频人重新识别(Reid)方法通常取决于全局级别功能。许多监督的Reid方法采用了本地级别的功能,并实现了显着的性能改进。但是,将本地级别的功能应用于无监督的方法可能会引入不稳定的性能。为了提高无监督视频REID的性能稳定,本文介绍了一般方案融合零件模型和无监督的学习。在该方案中,全局级别功能分为等于的本地级别。用于探索无监督学习的本地感知模块以探索对本地级别功能的概括。建议克服本地级别特征的缺点来克服全局感知模块。来自这两个模块的功能融合以形成每个输入图像的鲁棒特征表示。此特征表示具有本地级别功能的优点,而不会遭受其缺点。综合实验是在三个基准上进行的,包括PRID2011,ILIDS-VID和Dukemtmc-Videoreid,结果表明,该方法实现了最先进的性能。广泛的消融研究证明了所提出的计划,本地感知模块和全局感知模块的有效性和稳健性。
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由于其实际意义,跨情态人重新识别的问题已得到越来越多的关注。由于人类通常会在比较两个类似的物体时参加差异的事实,我们提出了一种双径跨模型特征学习框架,其保留了内在空间缩小,并参加了输入跨模型图像对的差异。我们的框架由两个主要组件组成:双路径空间结构保留公共空间网络(DSCSN)和对比相关网络(CCN)。前者将跨型号图像嵌入到共同的3D张量空间而不失去空间结构,而后者通过动态比较输入图像对提取对比特征。注意,为输入RGB和红外图像生成的表示彼此相互依赖。我们对两个公共可用RGB-IR REID数据集,SYSU-MM01和REGDB进行了广泛的实验,我们提出的方法优于完整和简化的评估模式的大边距优于最先进的算法。
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计算机视觉任务可以从估计突出物区域和这些对象区域之间的相互作用中受益。识别对象区域涉及利用预借鉴模型来执行对象检测,对象分割和/或对象姿势估计。但是,由于以下原因,在实践中不可行:1)预用模型的训练数据集的对象类别可能不会涵盖一般计算机视觉任务的所有对象类别,2)佩戴型模型训练数据集之间的域间隙并且目标任务的数据集可能会影响性能,3)预磨模模型中存在的偏差和方差可能泄漏到导致无意中偏置的目标模型的目标任务中。为了克服这些缺点,我们建议利用一系列视频帧捕获一组公共对象和它们之间的相互作用的公共基本原理,因此视频帧特征之间的共分割的概念可以用自动的能力装配模型专注于突出区域,以最终的方式提高潜在的任务的性能。在这方面,我们提出了一种称为“共分割激活模块”(COSAM)的通用模块,其可以被插入任何CNN,以促进基于CNN的任何CNN的概念在一系列视频帧特征中的关注。我们在三个基于视频的任务中展示Cosam的应用即1)基于视频的人Re-ID,2)视频字幕分类,并证明COSAM能够在视频帧中捕获突出区域,从而引导对于显着的性能改进以及可解释的关注图。
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最近,由于受监督人员重新识别(REID)的表现不佳,域名概括(DG)人REID引起了很多关注,旨在学习一个不敏感的模型,并可以抵抗域的影响偏见。在本文中,我们首先通过实验验证样式因素是域偏差的重要组成部分。基于这个结论,我们提出了一种样式变量且无关紧要的学习方法(SVIL)方法,以消除样式因素对模型的影响。具体来说,我们在SVIL中设计了样式的抖动模块(SJM)。 SJM模块可以丰富特定源域的样式多样性,并减少各种源域的样式差异。这导致该模型重点关注与身份相关的信息,并对样式变化不敏感。此外,我们将SJM模块与元学习算法有机结合,从而最大程度地提高了好处并进一步提高模型的概括能力。请注意,我们的SJM模块是插件和推理,无需成本。广泛的实验证实了我们的SVIL的有效性,而我们的方法的表现优于DG-REID基准测试的最先进方法。
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