Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is used to measure the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed natural and medical datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias.
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With the rapid increase of large-scale, real-world datasets, it becomes critical to address the problem of longtailed data distribution (i.e., a few classes account for most of the data, while most classes are under-represented). Existing solutions typically adopt class re-balancing strategies such as re-sampling and re-weighting based on the number of observations for each class. In this work, we argue that as the number of samples increases, the additional benefit of a newly added data point will diminish. We introduce a novel theoretical framework to measure data overlap by associating with each sample a small neighboring region rather than a single point. The effective number of samples is defined as the volume of samples and can be calculated by a simple formula (1−β n )/(1−β), where n is the number of samples and β ∈ [0, 1) is a hyperparameter. We design a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss. Comprehensive experiments are conducted on artificially induced long-tailed CIFAR datasets and large-scale datasets including ImageNet and iNaturalist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve significant performance gains on long-tailed datasets. * The work was performed while Yin Cui and Yang Song worked at Google (a subsidiary of Alphabet Inc.).
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与其他类别(称为少数族裔或尾巴类)相比,很少的类或类别(称为多数或头等类别的类别)具有更高的数据样本数量,在现实世界中,长尾数据集经常遇到。在此类数据集上培训深层神经网络会给质量级别带来偏见。到目前为止,研究人员提出了多种加权损失和数据重新采样技术,以减少偏见。但是,大多数此类技术都认为,尾巴类始终是最难学习的类,因此需要更多的重量或注意力。在这里,我们认为该假设可能并不总是成立的。因此,我们提出了一种新颖的方法,可以在模型的训练阶段动态测量每个类别的瞬时难度。此外,我们使用每个班级的难度度量来设计一种新型的加权损失技术,称为“基于阶级难度的加权(CDB-W)损失”和一种新型的数据采样技术,称为“基于类别难度的采样)(CDB-S )'。为了验证CDB方法的广泛可用性,我们对多个任务进行了广泛的实验,例如图像分类,对象检测,实例分割和视频操作分类。结果验证了CDB-W损失和CDB-S可以在许多类似于现实世界中用例的类别不平衡数据集(例如Imagenet-LT,LVIS和EGTEA)上实现最先进的结果。
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深度神经网络通常使用遇到数量不平衡和分类难度不平衡问题的数据集的性能很差。尽管在该领域取得了进展,但现有的两阶段方法中仍然存在数据集偏差或域转移问题。因此,提出了一个分阶段的渐进学习时间表,从而提出了从表示学习到上层分类器培训的平稳转移。这对严重失衡或较小尺度的数据集具有更大的有效性。设计了耦合 - 调节损失损失函数,耦合校正项,局灶性损失和LDAM损失。损失可以更好地处理数量不平衡和异常值,同时调节具有不同分类困难的样本的注意力重点。这些方法在多个基准数据集上取得了令人满意的结果,包括不平衡的CIFAR10,不平衡的CIFAR100,Imagenet-LT和Inaturalist 2018,并且还可以轻松地将其用于其他不平衡分类模型。
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少数族裔类的数据增强是长尾识别的有效策略,因此开发了大量方法。尽管这些方法都确保了样本数量的平衡,但是增强样品的质量并不总是令人满意的,识别且容易出现过度拟合,缺乏多样性,语义漂移等问题。对于这些问题,我们建议班级感知的大学启发了重新平衡学习(CAUIRR),以进行长尾识别,这使Universum具有班级感知的能力,可以从样本数量和质量中重新平衡个人少数族裔。特别是,我们从理论上证明,凯尔学到的分类器与从贝叶斯的角度从平衡状态下学到的那些人一致。此外,我们进一步开发了一种高阶混合方法,该方法可以自动生成类感知的Universum(CAU)数据,而无需诉诸任何外部数据。与传统的大学不同,此类产生的全球还考虑了域的相似性,阶级可分离性和样本多样性。基准数据集的广泛实验证明了我们方法的令人惊讶的优势,尤其是与最先进的方法相比,少数族裔类别的TOP1准确性提高了1.9%6%。
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大多数现有的最新视频分类方法假设训练数据遵守统一的分布。但是,现实世界中的视频数据通常会表现出不平衡的长尾巴分布,从而导致模型偏见对头等阶层,并且在尾巴上的性能相对较低。虽然当前的长尾分类方法通常集中在图像分类上,但将其调整到视频数据并不是微不足道的扩展。我们提出了一种端到端的多专家分布校准方法,以基于两级分布信息来应对这些挑战。该方法共同考虑了每个类别中样品的分布(类内部分布)和各种数据(类间分布)的总体分布,以解决在长尾分布下数据不平衡数据的问题。通过对两级分布信息进行建模,该模型可以共同考虑头等阶层和尾部类别,并将知识从头等阶层显着转移,以提高尾部类别的性能。广泛的实验验证了我们的方法是否在长尾视频分类任务上实现了最先进的性能。
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现实世界数据普遍面对严重的类别 - 不平衡问题,并且展示了长尾分布,即,大多数标签与有限的情况有关。由此类数据集监督的NA \“IVE模型更愿意占主导地位标签,遇到严重的普遍化挑战并变得不佳。我们从先前的角度提出了两种新的方法,以减轻这种困境。首先,我们推导了一个以平衡为导向的数据增强命名均匀的混合物(Unimix)促进长尾情景中的混合,采用先进的混合因子和采样器,支持少数民族。第二,受贝叶斯理论的动机,我们弄清了贝叶斯偏见(北美),是由此引起的固有偏见先前的不一致,并将其补偿为对标准交叉熵损失的修改。我们进一步证明了所提出的方法理论上和经验地确保分类校准。广泛的实验验证我们的策略是否有助于更好校准的模型,以及他们的策略组合在CIFAR-LT,ImageNet-LT和Inattations 2018上实现最先进的性能。
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类别不平衡数据的问题在于,由于少数类别的数据缺乏数据,分类器的泛化性能劣化。在本文中,我们提出了一种新的少数民族过度采样方法,通过利用大多数类作为背景图像的丰富背景来增加多元化的少数民族样本。为了使少数民族样本多样化,我们的主要思想是将前景补丁从少数级别粘贴到来自具有富裕环境的多数类的背景图像。我们的方法很简单,可以轻松地与现有的长尾识别方法结合。我们通过广泛的实验和消融研究证明了提出的过采样方法的有效性。如果没有任何架构更改或复杂的算法,我们的方法在各种长尾分类基准上实现了最先进的性能。我们的代码将在链接上公开提供。
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不平衡的数据对基于深度学习的分类模型构成挑战。解决不平衡数据的最广泛使用的方法之一是重新加权,其中训练样本与损失功能的不同权重相关。大多数现有的重新加权方法都将示例权重视为可学习的参数,并优化了元集中的权重,因此需要昂贵的双重优化。在本文中,我们从分布的角度提出了一种基于最佳运输(OT)的新型重新加权方法。具体而言,我们将训练集视为其样品上的不平衡分布,该分布由OT运输到从元集中获得的平衡分布。训练样品的权重是分布不平衡的概率质量,并通过最大程度地减少两个分布之间的ot距离来学习。与现有方法相比,我们提出的一种方法可以脱离每次迭代时的体重学习对相关分类器的依赖性。图像,文本和点云数据集的实验表明,我们提出的重新加权方法具有出色的性能,在许多情况下实现了最新的结果,并提供了一种有希望的工具来解决不平衡的分类问题。
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在这项工作中,我们解决了长尾图像识别的具有挑战性的任务。以前的长尾识别方法通常集中于尾巴类别的数据增强或重新平衡策略,以在模型培训期间更加关注尾巴类。但是,由于尾巴类别的训练图像有限,尾部类图像的多样性仍受到限制,从而导致特征表现不佳。在这项工作中,我们假设头部和尾部类中的常见潜在特征可用于提供更好的功能表示。由此激励,我们引入了基于潜在类别的长尾识别(LCREG)方法。具体来说,我们建议学习一组在头和尾巴中共享的类不足的潜在特征。然后,我们通过将语义数据扩展应用于潜在特征,隐式地丰富了训练样本的多样性。对五个长尾图识别数据集进行的广泛实验表明,我们提出的LCREG能够显着超越先前的方法并实现最新结果。
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Deep neural networks still struggle on long-tailed image datasets, and one of the reasons is that the imbalance of training data across categories leads to the imbalance of trained model parameters. Motivated by the empirical findings that trained classifiers yield larger weight norms in head classes, we propose to reformulate the recognition probabilities through included angles without re-balancing the classifier weights. Specifically, we calculate the angles between the data feature and the class-wise classifier weights to obtain angle-based prediction results. Inspired by the performance improvement of the predictive form reformulation and the outstanding performance of the widely used two-stage learning framework, we explore the different properties of this angular prediction and propose novel modules to improve the performance of different components in the framework. Our method is able to obtain the best performance among peer methods without pretraining on CIFAR10/100-LT and ImageNet-LT. Source code will be made publicly available.
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长尾数据集(Head Class)组成的培训样本比尾巴类别多得多,这会导致识别模型对头等舱有偏见。加权损失是缓解此问题的最受欢迎的方法之一,最近的一项工作表明,班级难度可能比常规使用的类频率更好地决定了权重的分布。在先前的工作中使用了一种启发式公式来量化难度,但是我们从经验上发现,最佳公式取决于数据集的特征。因此,我们提出了困难网络,该难题学习在元学习框架中使用模型的性能来预测类的难度。为了使其在其他班级的背景下学习班级的合理难度,我们新介绍了两个关键概念,即相对难度和驾驶员损失。前者有助于困难网络在计算班级难度时考虑其他课程,而后者对于将学习指向有意义的方向是必不可少的。对流行的长尾数据集进行了广泛的实验证明了该方法的有效性,并且在多个长尾数据集上实现了最先进的性能。
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Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.
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现实世界中的数据通常遵循长尾巴的分布,其中一些多数类别占据了大多数数据,而大多数少数族裔类别都包含有限数量的样本。分类模型最小化跨凝结的努力来代表和分类尾部类别。尽管已经对学习无偏分类器的学习问题进行了充分的研究,但代表不平衡数据的方法却没有探索。在本文中,我们专注于表示不平衡数据的表示。最近,受到监督的对比学习最近在平衡数据上表现出了有希望的表现。但是,通过我们的理论分析,我们发现对于长尾数据,它未能形成常规的单纯形,这是代表学习的理想几何配置。为了纠正SCL的优化行为并进一步改善了长尾视觉识别的性能,我们提出了平衡对比度学习(BCL)的新型损失。与SCL相比,我们在BCL:类平均水平方面有两个改进,可以平衡负类的梯度贡献。课堂组合,允许所有类都出现在每个迷你批次中。提出的平衡对比度学习(BCL)方法满足形成常规单纯形的条件并有助于跨透明拷贝的优化。配备了BCL,提出的两分支框架可以获得更强的特征表示,并在诸如CIFAR-10-LT,CIFAR-100-LT,Imagenet-LT和Inaturalist2018之类的长尾基准数据集上实现竞争性能。我们的代码可在\ href {https://github.com/flamiezhu/bcl} {this url}中获得。
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Our work focuses on tackling the challenging but natural visual recognition task of long-tailed data distribution (i.e., a few classes occupy most of the data, while most classes have rarely few samples). In the literature, class re-balancing strategies (e.g., re-weighting and re-sampling) are the prominent and effective methods proposed to alleviate the extreme imbalance for dealing with long-tailed problems. In this paper, we firstly discover that these rebalancing methods achieving satisfactory recognition accuracy owe to that they could significantly promote the classifier learning of deep networks. However, at the same time, they will unexpectedly damage the representative ability of the learned deep features to some extent. Therefore, we propose a unified Bilateral-Branch Network (BBN) to take care of both representation learning and classifier learning simultaneously, where each branch does perform its own duty separately. In particular, our BBN model is further equipped with a novel cumulative learning strategy, which is designed to first learn the universal patterns and then pay attention to the tail data gradually. Extensive experiments on four benchmark datasets, including the large-scale iNaturalist ones, justify that the proposed BBN can significantly outperform state-of-the-art methods. Furthermore, validation experiments can demonstrate both our preliminary discovery and effectiveness of tailored designs in BBN for long-tailed problems. Our method won the first place in the iNaturalist 2019 large scale species classification competition, and our code is open-source and available at https://github.com/Megvii-Nanjing/BBN . * Q. Cui and Z.-M. Chen's contribution was made when they were interns in Megvii Research Nanjing, Megvii Technology, China. X.
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现实世界数据通常遵循长尾分布,这使得现有分类算法的性能较大。关键问题是尾类别中的样本未能描绘其级别的多种多样性。人类可以想象在新的姿势,场景和观看角度的样本,即使是第一次看到此类别也是如此。灵感来自于此,我们提出了一种新的基于推理的隐式语义数据增强方法,可以从其他类借用转换方向。由于每个类别的协方差矩阵表示特征转换方向,因此我们可以从类似类别中采样新的方向以产生绝对不同的实例。具体地,首先采用长尾分布式数据来训练骨干和分类器。然后,估计每个类别的协方差矩阵,构建知识图形以存储任何两个类别的关系。最后,通过从知识图中的所有类似类别传播信息,自适应地增强尾样本。 CiFar-100-LT,想象 - LT和Inattations 2018上的实验结果表明了我们所提出的方法的有效性与最先进的方法相比。
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长尾数据集的泛化差距主要是由于大多数类别仅占占用几个训练样本。解耦培训通过分别培训骨干和分类器来实现更好的性能。导致端到端模型培训的较差的性能(例如,基于Logits利润率的方法)?在这项工作中,我们确定影响分类器的学习的关键因素:在输入分类器之前,具有低熵的通道相关功能。从信息理论的角度来看,我们分析了为什么交叉熵损失倾向于在不平衡数据上产生高度相关的特征。此外,我们理论上的分析和证明对分类器权重的梯度,Hessian的条件数量的影响,以及基于利润率的方法的影响。因此,我们首先建议使用频道美白与去相关(“散点”)分类器的输入用于解耦的权重更新和重塑偏移决策边界,这使得令人满意的结果与基于Logits裕度的方法相结合。但是,当小类课程的数量大,批量不平衡和更多的参与训练导致主要类的过度拟合。我们还提出了两种新颖的模块,基于块的相对平衡的批量采样器(B3RS)和批量嵌入式培训(BET)来解决上述问题,这使得端到端的训练能够实现比解耦训练更好的性能。在长尾分类基准测试,CIFAR-LT和Imagenet-LT上的实验结果证明了我们方法的有效性。
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A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.
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深度神经网络在严重的类不平衡数据集上的表现不佳。鉴于对比度学习的有希望的表现,我们提出了重新平衡的暹罗对比度采矿(RESCOM)来应对不平衡的识别。基于数学分析和仿真结果,我们声称监督的对比学习在原始批次和暹罗批次水平上都遭受双重失衡问题,这比长尾分类学习更为严重。在本文中,在原始批处理水平上,我们引入了级别平衡的监督对比损失,以分配不同类别的自适应权重。在暹罗批次级别,我们提出了一个级别平衡的队列,该队列维持所有类的键相同。此外,我们注意到,相对于对比度逻辑的不平衡对比损失梯度可以将其分解为阳性和负面因素,易于阳性和易于负面因素将使对比度梯度消失。我们建议有监督的正面和负面对挖掘,以获取信息对的对比度计算并改善表示形式学习。最后,为了大致最大程度地提高两种观点之间的相互信息,我们提出了暹罗平衡的软性软件,并与一阶段训练的对比损失结合。广泛的实验表明,在多个长尾识别基准上,RESCON优于先前的方法。我们的代码和模型可公开可用:https://github.com/dvlab-research/rescom。
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我们提出了一种称为分配 - 均衡损失的新损失功能,用于展示长尾类分布的多标签识别问题。与传统的单标分类问题相比,由于两个重要问题,多标签识别问题通常更具挑战性,即标签的共同发生以及负标签的主导地位(当被视为多个二进制分类问题时)。分配 - 平衡损失通过对标准二进制交叉熵丢失的两个关键修改来解决这些问题:1)重新平衡考虑标签共发生造成的影响的重量的新方法,以及2)负耐受规则化以减轻负标签的过度抑制。 Pascal VOC和Coco的实验表明,使用这种新损失功能训练的模型可实现现有方法的显着性能。代码和型号可在:https://github.com/wutong16/distributionbalancedloss。
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