长尾学习旨在应对在现实情况下严重的阶级失衡下统治训练程序的关键挑战。但是，很少有人注意如何量化表示空间中头等的优势严重性。在此激励的情况下，我们将基于余弦的分类器推广到von mises-fisher（VMF）混合模型，该模型被称为VMF分类器，该模型可以通过计算分布重叠系数来定量地测量超晶体空间上的表示质量。据我们所知，这是从分布重叠系数的角度来衡量分类器和特征的表示质量的第一项工作。最重要的是，我们制定了类间差异和类功能的一致性损失项，以减轻分类器的重量之间的干扰，并与分类器的权重相结合。此外，一种新型的训练后校准算法设计为零成本通过类间重叠系数来提高性能。我们的方法的表现优于先前的工作，并具有很大的利润，并在长尾图像分类，语义细分和实例分段任务上实现了最先进的性能（例如，我们在Imagenet-50中实现了55.0 \％的总体准确性LT）。我们的代码可在https://github.com/vipailab/vmf \_op上找到。

As the class size grows, maintaining a balanced dataset across many classes is challenging because the data are long-tailed in nature; it is even impossible when the sample-of-interest co-exists with each other in one collectable unit, e.g., multiple visual instances in one image. Therefore, long-tailed classification is the key to deep learning at scale. However, existing methods are mainly based on reweighting/re-sampling heuristics that lack a fundamental theory. In this paper, we establish a causal inference framework, which not only unravels the whys of previous methods, but also derives a new principled solution. Specifically, our theory shows that the SGD momentum is essentially a confounder in long-tailed classification. On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head. On the other hand, its induced mediation also benefits the representation learning and head prediction. Our framework elegantly disentangles the paradoxical effects of the momentum, by pursuing the direct causal effect caused by an input sample. In particular, we use causal intervention in training, and counterfactual reasoning in inference, to remove the "bad" while keep the "good". We achieve new state-of-the-arts on three long-tailed visual recognition benchmarks 1 : Long-tailed CIFAR-10/-100, ImageNet-LT for image classification and LVIS for instance segmentation.

长尾分布是现实世界中的常见现象。提取的大规模图像数据集不可避免地证明了长尾巴的属性和经过不平衡数据训练的模型可以为代表性过多的类别获得高性能，但为代表性不足的类别而苦苦挣扎，导致偏见的预测和绩效降低。为了应对这一挑战，我们提出了一种名为“逆图像频率”（IIF）的新型偏差方法。 IIF是卷积神经网络分类层中逻辑的乘法边缘调整转换。我们的方法比类似的作品实现了更强的性能，并且对于下游任务（例如长尾实例分割）特别有用，因为它会产生较少的假阳性检测。我们的广泛实验表明，IIF在许多长尾基准的基准（例如Imagenet-lt，cifar-lt，ploce-lt和lvis）上超过了最先进的现状，在Imagenet-lt上，Resnet50和26.2％达到了55.8％的TOP-1准确性LVIS上使用MaskRCNN分割AP。代码可在https://github.com/kostas1515/iif中找到

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。

The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strategies, e.g. by loss re-weighting, data re-sampling, or transfer learning from head-to tail-classes, but most of them adhere to the scheme of jointly learning representations and classifiers. In this work, we decouple the learning procedure into representation learning and classification, and systematically explore how different balancing strategies affect them for long-tailed recognition. The findings are surprising: (1) data imbalance might not be an issue in learning high-quality representations; (2) with representations learned with the simplest instance-balanced (natural) sampling, it is also possible to achieve strong long-tailed recognition ability by adjusting only the classifier. We conduct extensive experiments and set new state-of-the-art performance on common long-tailed benchmarks like ImageNet-LT, Places-LT and iNaturalist, showing that it is possible to outperform carefully designed losses, sampling strategies, even complex modules with memory, by using a straightforward approach that decouples representation and classification. Our code is available at https://github.com/facebookresearch/classifier-balancing.

最近在对象检测和细分领域取得了重大进步。但是，当涉及到罕见类别时，最新方法无法检测到它们，从而在稀有类别和频繁类别之间存在显着的性能差距。在本文中，我们确定深探测器中使用的Sigmoid或SoftMax函数是低性能的主要原因，并且是长尾检测和分割的最佳选择。为了解决这个问题，我们开发了牙龈优化的损失（GOL），以进行长尾检测和分割。考虑到大多数长尾检测中的大多数类的预期概率较低，它与数据集中罕见类别的牙胶分布保持一致。拟议的GOL在AP上显着优于最佳最新方法的最佳方法，并将整体分割率提高9.0％，并将检测到8.0％，尤其是将稀有类别的检测提高了20.3％，与Mask-Rcnn相比提高了20.3％。 ，在LVIS数据集上。代码可用：https：//github.com/kostas1515/gol

Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on large vocabulary and long-tailed datasets, e.g. LVIS.In this work, we analyze this problem from a novel perspective: each positive sample of one category can be seen as a negative sample for other categories, making the tail categories receive more discouraging gradients. Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories. The equalization loss protects the learning of rare categories from being at a disadvantage during the network parameter updating. Thus the model is capable of learning better discriminative features for objects of rare classes. Without any bells and whistles, our method achieves AP gains of 4.1% and 4.8% for the rare and common categories on the challenging LVIS benchmark, compared to the Mask R-CNN baseline. With the utilization of the effective equalization loss, we finally won the 1st place in the LVIS Challenge 2019. Code has been made available at: https: //github.com/tztztztztz/eql.detectron2

Vanilla用于物体检测和实例分割的模型遭受重偏向朝着长尾设置中的频繁对象进行偏向。现有方法主要在培训期间解决此问题，例如，通过重新采样或重新加权。在本文中，我们调查了一个很大程度上被忽视的方法 - 置信分数的后处理校准。我们提出NORCAL，用于长尾对象检测和实例分割的归一化校准校准，简单而简单的配方，通过其训练样本大小重新恢复每个阶级的预测得分。我们展示了单独处理背景类并使每个提案的课程分数标准化是实现卓越性能的键。在LVIS DataSet上，Norcal不仅可以在罕见的课程上有效地改善所有基线模型，也可以在普通和频繁的阶级上改进。最后，我们进行了广泛的分析和消融研究，以了解我们方法的各种建模选择和机制的见解。我们的代码在https://github.com/tydpan/norcal/上公开提供。

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。

现实世界数据通常存在长尾分布。对不平衡数据的培训倾向于呈现神经网络在头部上表现良好，而尾部课程则更加差。尾班的培训实例的严重稀疏性是主要挑战，这导致培训期间的偏见分配估计。丰富的努力已经致力于改善挑战，包括数据重新采样和综合尾班的新培训实例。然而，没有先前的研究已经利用了从头课程转移到尾班的可转让知识，以校准尾舱的分布。在本文中，我们假设可以通过类似的头部级别来丰富尾部类，并提出一种名为标签感知分布校准Ladc的新型分布校准方法。 Ladc从相关的头部课程转移统计数据以推断尾部课程的分布。从校准分布的采样进一步促进重新平衡分类器。图像和文本的实验和文本长尾数据集表明，LADC显着优于现有方法。可视化还显示LADC提供更准确的分布估计。

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。

在本文中，我们提出了广义参数对比度学习（GPACO/PACO），该学习在不平衡和平衡数据上都很好地工作。基于理论分析，我们观察到，受监督的对比损失倾向于偏向高频类别，从而增加了学习不平衡的学习难度。我们从优化的角度介绍了一组参数班的可学习中心，以重新平衡。此外，我们在平衡的环境下分析了GPACO/PACO损失。我们的分析表明，GPACO/PACO可以适应地增强同一等级样品的强度，因为将更多的样品与相应的中心一起拉在一起并有益于艰难的示例学习。长尾基准测试的实验表明了长尾识别的新最先进。在完整的Imagenet上，与MAE模型相比，从CNN到接受GPACO损失训练的视觉变压器的模型显示出更好的泛化性能和更强的鲁棒性。此外，GPACO可以应用于语义分割任务，并在4个最受欢迎的基准测试中观察到明显的改进。我们的代码可在https://github.com/dvlab-research/parametric-contrastive-learning上找到。

由于课程中的训练样本极端不平衡，长尾实例分割是一个具有挑战性的任务。它导致头部课程的严重偏差（含有多数样本）对尾尾。这呈现“如何适当地定义和缓解偏见”最重要的问题之一。先前作品主要使用标签分布或平均分数信息来表示粗粒偏置。在本文中，我们探索挖掘困难的矩阵，该矩阵携带细粒度的错误分类细节，以减轻成对偏置，概括粗液。为此，我们提出了一种新颖的成对类余额（PCB）方法，基于混淆矩阵，在训练期间更新以累积正在进行的预测偏好。 PCB在培训期间生成正规化的纠错软标签。此外，开发了一种迭代学习范例，以支持这种脱结的渐进和平稳的正则化。 PCB可以插入并播放任何现有方法作为补充。 LVIS的实验结果表明，我们的方法在没有钟声和口哨的情况下实现最先进的性能。各种架构的卓越结果表明了泛化能力。

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。

Recent studies have revealed that, beyond conventional accuracy, calibration should also be considered for training modern deep neural networks. To address miscalibration during learning, some methods have explored different penalty functions as part of the learning objective, alongside a standard classification loss, with a hyper-parameter controlling the relative contribution of each term. Nevertheless, these methods share two major drawbacks: 1) the scalar balancing weight is the same for all classes, hindering the ability to address different intrinsic difficulties or imbalance among classes; and 2) the balancing weight is usually fixed without an adaptive strategy, which may prevent from reaching the best compromise between accuracy and calibration, and requires hyper-parameter search for each application. We propose Class Adaptive Label Smoothing (CALS) for calibrating deep networks, which allows to learn class-wise multipliers during training, yielding a powerful alternative to common label smoothing penalties. Our method builds on a general Augmented Lagrangian approach, a well-established technique in constrained optimization, but we introduce several modifications to tailor it for large-scale, class-adaptive training. Comprehensive evaluation and multiple comparisons on a variety of benchmarks, including standard and long-tailed image classification, semantic segmentation, and text classification, demonstrate the superiority of the proposed method. The code is available at https://github.com/by-liu/CALS.

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。

长尾图像识别对深度学习系统提出了巨大的挑战，因为多数（头）类别与少数族裔（TAIL）类之间的失衡严重偏斜了数据驱动的深度神经网络。以前的方法从数据分布，功能空间和模型设计等的角度来解决数据失衡。从以前省略的平衡标签空间的角度来看。为了减轻从头到尾的偏见，我们通过逐步调整标签空间并将头等阶层和尾部类别分开，动态构建平衡从不平衡到促进分类，提出简洁的范式。借助灵活的数据过滤和标签空间映射，我们可以轻松地将方法嵌入大多数分类模型，尤其是脱钩的训练方法。此外，我们发现头尾类别的可分离性在具有不同电感偏见的不同特征之间各不相同。因此，我们提出的模型还提供了一种功能评估方法，并为长尾特征学习铺平了道路。广泛的实验表明，我们的方法可以在广泛使用的基准上提高不同类型的最先进的性能。代码可在https://github.com/silicx/dlsa上找到。

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.

The real-world data tends to be heavily imbalanced and severely skew the data-driven deep neural networks, which makes Long-Tailed Recognition (LTR) a massive challenging task. Existing LTR methods seldom train Vision Transformers (ViTs) with Long-Tailed (LT) data, while the off-the-shelf pretrain weight of ViTs always leads to unfair comparisons. In this paper, we systematically investigate the ViTs' performance in LTR and propose LiVT to train ViTs from scratch only with LT data. With the observation that ViTs suffer more severe LTR problems, we conduct Masked Generative Pretraining (MGP) to learn generalized features. With ample and solid evidence, we show that MGP is more robust than supervised manners. In addition, Binary Cross Entropy (BCE) loss, which shows conspicuous performance with ViTs, encounters predicaments in LTR. We further propose the balanced BCE to ameliorate it with strong theoretical groundings. Specially, we derive the unbiased extension of Sigmoid and compensate extra logit margins to deploy it. Our Bal-BCE contributes to the quick convergence of ViTs in just a few epochs. Extensive experiments demonstrate that with MGP and Bal-BCE, LiVT successfully trains ViTs well without any additional data and outperforms comparable state-of-the-art methods significantly, e.g., our ViT-B achieves 81.0% Top-1 accuracy in iNaturalist 2018 without bells and whistles. Code is available at https://github.com/XuZhengzhuo/LiVT.

Significant progress has been made in learning image classification neural networks under long-tail data distribution using robust training algorithms such as data re-sampling, re-weighting, and margin adjustment. Those methods, however, ignore the impact of data imbalance on feature normalization. The dominance of majority classes (head classes) in estimating statistics and affine parameters causes internal covariate shifts within less-frequent categories to be overlooked. To alleviate this challenge, we propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. In addition, a moving average-based expectation maximization (EM) algorithm is employed to estimate the statistical parameters of multiple Gaussian distributions. However, the EM algorithm is sensitive to initialization and can easily become stuck in local minima where the multiple Gaussian components continue to focus on majority classes. To tackle this issue, we developed a dual-path learning framework that employs class-aware split feature normalization to diversify the estimated Gaussian distributions, allowing the Gaussian components to fit with training samples of less-frequent classes more comprehensively. Extensive experiments on commonly used datasets demonstrated that the proposed method outperforms existing methods on long-tailed image classification.