This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity s p and minimize the between-class similarity s n . We find a majority of loss functions, including the triplet loss and the softmax cross-entropy loss, embed s n and s p into similarity pairs and seek to reduce (s n − s p ). Such an optimization manner is inflexible, because the penalty strength on every single similarity score is restricted to be equal. Our intuition is that if a similarity score deviates far from the optimum, it should be emphasized. To this end, we simply re-weight each similarity to highlight the less-optimized similarity scores. It results in a Circle loss, which is named due to its circular decision boundary. The Circle loss has a unified formula for two elemental deep feature learning paradigms, i.e., learning with class-level labels and pair-wise labels. Analytically, we show that the Circle loss offers a more flexible optimization approach towards a more definite convergence target, compared with the loss functions optimizing (s n − s p ). Experimentally, we demonstrate the superiority of the Circle loss on a variety of deep feature learning tasks. On face recognition, person re-identification, as well as several finegrained image retrieval datasets, the achieved performance is on par with the state of the art.

Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative example while not interacting with the other negative classes in each update. In this paper, we propose to address this problem with a new metric learning objective called multi-class N -pair loss. The proposed objective function firstly generalizes triplet loss by allowing joint comparison among more than one negative examples -more specifically, N -1 negative examples -and secondly reduces the computational burden of evaluating deep embedding vectors via an efficient batch construction strategy using only N pairs of examples, instead of (N +1)×N . We demonstrate the superiority of our proposed loss to the triplet loss as well as other competing loss functions for a variety of tasks on several visual recognition benchmark, including fine-grained object recognition and verification, image clustering and retrieval, and face verification and identification.

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge show the superiority of A-Softmax loss in FR tasks. The code has also been made publicly available 1 .

最先进的面部识别方法通常采用多分类管道，并采用基于SoftMax的损耗进行优化。虽然这些方法取得了巨大的成功，但基于Softmax的损失在开放式分类的角度下有其限制：训练阶段的多分类目标并没有严格匹配开放式分类测试的目标。在本文中，我们派生了一个名为全局边界Cosface的新损失（GB-Cosface）。我们的GB-COSface介绍了自适应全局边界，以确定两个面积是否属于相同的身份，使得优化目标与从开放集分类的角度与测试过程对齐。同时，由于损失配方来自于基于软MAX的损失，因此我们的GB-COSFace保留了基于软MAX的损耗的优异性能，并且证明了COSFace是拟议损失的特殊情况。我们在几何上分析并解释了所提出的GB-Cosface。多面识别基准测试的综合实验表明，所提出的GB-Cosface优于主流面部识别任务中的当前最先进的面部识别损失。与Cosface相比，我们的GB-Cosface在Tar @ Far = 1E-6，1E-5，1E-4上提高了1.58％，0.57％和0.28％的IJB-C基准。

Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions such as center loss, large margin softmax loss, and angular softmax loss have been proposed. All these improved losses share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we propose a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as a cosine loss by L 2 normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by virtue of normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. Extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmarks, which confirms the effectiveness of our proposed approach.

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains K sub-centers and training samples only need to be close to any of the K positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis.

In this paper, we propose a conceptually simple and geometrically interpretable objective function, i.e. additive margin Softmax (AM-Softmax), for deep face verification. In general, the face verification task can be viewed as a metric learning problem, so learning large-margin face features whose intra-class variation is small and inter-class difference is large is of great importance in order to achieve good performance. Recently, Large-margin Softmax [10] and Angular Softmax [9] have been proposed to incorporate the angular margin in a multiplicative manner. In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works. We also emphasize and discuss the importance of feature normalization in the paper. Most importantly, our experiments on LFW and MegaFace show that our additive margin softmax loss consistently performs better than the current state-of-the-art methods using the same network architecture and training dataset. Our code has also been made available 1 .

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.

本文从跨模式度量学习的角度来解决基于零点草图的图像检索（ZS-SBIR）问题。此任务具有两个特性：1）零拍摄设置需要具有良好的课堂紧凑性和识别新颖类别的课堂间差异的度量空间，而2）草图查询和照片库是不同的模态。从两个方面，公制学习视点益处ZS-SBIR。首先，它促进了深度度量学习（DML）中最近的良好实践的改进。通过在DML中结合两种基本学习方法，例如分类培训和成对培训，我们为ZS-SBIR设置了一个强大的基线。没有钟声和口哨，这种基线实现了竞争的检索准确性。其次，它提供了一个正确抑制模态间隙至关重要的洞察力。为此，我们设计了一种名为Domency Ippar Triplet硬挖掘（Mathm）的新颖方法。 Mathm增强了基线，具有三种类型的成对学习，例如跨模型样本对，模态样本对，以及它们的组合。\我们还设计了一种自适应加权方法，可以在动态训练期间平衡这三个组件。实验结果证实，Mathm根据强大的基线带来另一轮显着改进，并建立了新的最先进的性能。例如，在Tu-Berlin数据集上，我们达到了47.88 + 2.94％地图@全部和58.28 + 2.34％prip @ 100。代码将在：https://github.com/huangzongheng/mathm公开使用。

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other. In this work, we recognize that there is a significant semantic gap between features at the intermediate feature layer and class labels at the final output layer. To bridge this gap, we develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity in a contrastive learning setting. This contrastive Bayesian analysis leads to a new loss function for deep metric learning. To improve the generalization capability of the proposed method onto new classes, we further extend the contrastive Bayesian loss with a metric variance constraint. Our experimental results and ablation studies demonstrate that the proposed contrastive Bayesian metric learning method significantly improves the performance of deep metric learning in both supervised and pseudo-supervised scenarios, outperforming existing methods by a large margin.

学习歧视性面部特征在建立高性能面部识别模型方面发挥着重要作用。最近的最先进的面部识别解决方案，提出了一种在常用的分类损失函数，Softmax损失中纳入固定的惩罚率，通过最大限度地减少级别的变化来增加面部识别模型的辨别力并最大化级别的帧间变化。边缘惩罚Softmax损失，如arcFace和Cosface，假设可以使用固定的惩罚余量同样地学习不同身份之间的测地距。然而，这种学习目标对于具有不一致的间帧内变化的真实数据并不是现实的，这可能限制了面部识别模型的判别和概括性。在本文中，我们通过提出弹性罚款损失（弹性面）来放松固定的罚款边缘约束，这允许在推动阶级可分离性中灵活性。主要思想是利用从每个训练迭代中的正常分布中汲取的随机保证金值。这旨在提供决策边界机会，以提取和缩回，以允许灵活的类别可分离学习的空间。我们展示了在大量主流基准上使用相同的几何变换，展示了我们的弹性面损失和COSFace损失的优势。从更广泛的角度来看，我们的弹性面在九个主流基准中提出了最先进的面部识别性能。

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.

深度度量学习（DML）有助于学习嵌入功能，以将语义上的数据投射到附近的嵌入空间中，并在许多应用中起着至关重要的作用，例如图像检索和面部识别。但是，DML方法的性能通常很大程度上取决于采样方法，从训练中的嵌入空间中选择有效的数据。实际上，嵌入空间中的嵌入是通过一些深层模型获得的，其中嵌入空间通常由于缺乏训练点而在贫瘠的区域中，导致所谓的“缺失嵌入”问题。此问题可能会损害样品质量，从而导致DML性能退化。在这项工作中，我们研究了如何减轻“缺失”问题以提高采样质量并实现有效的DML。为此，我们提出了一个密集锚定的采样（DAS）方案，该方案将嵌入的数据点视为“锚”，并利用锚附近的嵌入空间来密集地生成无数据点的嵌入。具体而言，我们建议用判别性特征缩放（DFS）和多个锚点利用单个锚周围的嵌入空间，并具有记忆转换转换（MTS）。通过这种方式，通过有或没有数据点的嵌入方式，我们能够提供更多的嵌入以促进采样过程，从而提高DML的性能。我们的方法毫不费力地集成到现有的DML框架中，并在没有铃铛和哨声的情况下改进了它们。在三个基准数据集上进行的广泛实验证明了我们方法的优势。

基于软马克斯的损失函数及其变体（例如，界面，圆顶和弧形）可显着改善野生无约束场景中的面部识别性能。这些算法的一种常见实践是对嵌入特征和线性转换矩阵之间的乘法进行优化。但是，在大多数情况下，基于传统的设计经验给出了嵌入功能的尺寸，并且在给出固定尺寸时，使用该功能本身提高性能的研究较少。为了应对这一挑战，本文提出了一种称为subface的软关系近似方法，该方法采用了子空间功能来促进面部识别的性能。具体而言，我们在训练过程中动态选择每个批次中的非重叠子空间特征，然后使用子空间特征在基于软磁性的损失之间近似完整功能，因此，深层模型的可区分性可以显着增强，以增强面部识别。在基准数据集上进行的综合实验表明，我们的方法可以显着提高香草CNN基线的性能，这强烈证明了基于利润率的损失的子空间策略的有效性。

深度度量学习（DML）了解映射，该映射到嵌入空间，其中类似数据接近并且不同的数据远远。然而，DML的传统基于代理的损失有两个问题：渐变问题并使用多个本地中心应用现实世界数据集。此外，DML性能指标也有一些问题具有稳定性和灵活性。本文提出了多代理锚（MPA）丢失和归一化折扣累积增益（NDCG @ K）度量。本研究贡献了三个以下：（1）MPA损失能够使用多代理学习现实世界数据集。（2）MPA损失提高了神经网络的培训能力，解决了梯度问题。（3）NDCG @ K度量标准鼓励对各种数据集进行全面评估。最后，我们展示了MPA损失的有效性，MPA损失在两个用于细粒度图像的数据集上实现了最高准确性。

Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search. The most prominent approaches optimize a deep convolutional network with a suitable loss function, such as contrastive loss or triplet loss. While a rich line of work focuses solely on the loss functions, we show in this paper that selecting training examples plays an equally important role. We propose distance weighted sampling, which selects more informative and stable examples than traditional approaches. In addition, we show that a simple margin based loss is sufficient to outperform all other loss functions. We evaluate our approach on the Stanford Online Products, CAR196, and the CUB200-2011 datasets for image retrieval and clustering, and on the LFW dataset for face verification. Our method achieves state-of-the-art performance on all of them.

Deep Metric Learning (DML) learns a non-linear semantic embedding from input data that brings similar pairs together while keeping dissimilar data away from each other. To this end, many different methods are proposed in the last decade with promising results in various applications. The success of a DML algorithm greatly depends on its loss function. However, no loss function is perfect, and it deals only with some aspects of an optimal similarity embedding. Besides, the generalizability of the DML on unseen categories during the test stage is an important matter that is not considered by existing loss functions. To address these challenges, we propose novel approaches to combine different losses built on top of a shared deep feature extractor. The proposed ensemble of losses enforces the deep model to extract features that are consistent with all losses. Since the selected losses are diverse and each emphasizes different aspects of an optimal semantic embedding, our effective combining methods yield a considerable improvement over any individual loss and generalize well on unseen categories. Here, there is no limitation in choosing loss functions, and our methods can work with any set of existing ones. Besides, they can optimize each loss function as well as its weight in an end-to-end paradigm with no need to adjust any hyper-parameter. We evaluate our methods on some popular datasets from the machine vision domain in conventional Zero-Shot-Learning (ZSL) settings. The results are very encouraging and show that our methods outperform all baseline losses by a large margin in all datasets.

我们研究人员重新识别（RE-ID）的向后兼容问题，该问题旨在限制更新的新模型的功能，以与画廊中旧模型的现有功能相提并论。大多数现有作品都采用基于蒸馏的方法，这些方法着重于推动新功能模仿旧功能。但是，基于蒸馏的方法本质上是最佳的，因为它迫使新的特征空间模仿旧特征空间。为了解决这个问题，我们提出了基于排名的向后兼容学习（RBCL），该学习直接优化了新功能和旧功能之间的排名指标。与以前的方法不同，RBCL仅推动新功能以在旧功能空间而不是严格对齐中找到最佳的位置，并且与向后检索的最终目标保持一致。但是，用于使排名度量可区分的尖锐的Sigmoid函数也会导致梯度消失的问题，因此在训练后期的时期造成了排名的完善。为了解决这个问题，我们提出了动态梯度重新激活（DGR），可以通过在远期步骤中添加动态计算的常数来重新激活抑制梯度。为了进一步帮助目标最佳位置，我们包括邻居上下文代理（NCAS），以近似训练期间的整个旧特征空间。与以前仅在内域设置上测试的作品不同，我们首次尝试引入跨域设置（包括受监督和无监督的），这更有意义和困难。所有五个设置上的实验结果表明，在所有设置下，提出的RBCL都以大幅度优于先前的最新方法。

大多数深度度量学习（DML）方法采用了一种策略，该策略迫使所有积极样本在嵌入空间中靠近，同时使它们远离负面样本。但是，这种策略忽略了正（负）样本的内部关系，并且通常导致过度拟合，尤其是在存在硬样品和标签错误的情况下。在这项工作中，我们提出了一个简单而有效的正则化，即列表自我验证（LSD），该化逐渐提炼模型的知识，以适应批处理中每个样本对的更合适的距离目标。LSD鼓励在正（负）样本中更平稳的嵌入和信息挖掘，以减轻过度拟合并从而改善概括。我们的LSD可以直接集成到一般的DML框架中。广泛的实验表明，LSD始终提高多个数据集上各种度量学习方法的性能。

In this paper, we investigate the problem of predictive confidence in face and kinship verification. Most existing face and kinship verification methods focus on accuracy performance while ignoring confidence estimation for their prediction results. However, confidence estimation is essential for modeling reliability in such high-risk tasks. To address this issue, we first introduce a novel yet simple confidence measure for face and kinship verification, which allows the verification models to transform the similarity score into a confidence score for a given face pair. We further propose a confidence-calibrated approach called angular scaling calibration (ASC). ASC is easy to implement and can be directly applied to existing face and kinship verification models without model modifications, yielding accuracy-preserving and confidence-calibrated probabilistic verification models. To the best of our knowledge, our approach is the first general confidence-calibrated solution to face and kinship verification in a modern context. We conduct extensive experiments on four widely used face and kinship verification datasets, and the results demonstrate the effectiveness of our approach.