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.
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A family of loss functions built on pair-based computation have been proposed in the literature which provide a myriad of solutions for deep metric learning. In this paper, we provide a general weighting framework for understanding recent pair-based loss functions. Our contributions are three-fold: (1) we establish a General Pair Weighting (GPW) framework, which casts the sampling problem of deep metric learning into a unified view of pair weighting through gradient analysis, providing a powerful tool for understanding recent pair-based loss functions; (2) we show that with GPW, various existing pair-based methods can be compared and discussed comprehensively, with clear differences and key limitations identified; (3) we propose a new loss called multi-similarity loss (MS loss) under the GPW, which is implemented in two iterative steps (i.e., mining and weighting). This allows it to fully consider three similarities for pair weighting, providing a more principled approach for collecting and weighting informative pairs. Finally, the proposed MS loss obtains new state-of-the-art performance on four image retrieval benchmarks, where it outperforms the most recent approaches, such as ABE [14] and HTL [4], by a large margin, e.g., , and 80.9% → 88.0% on In-Shop Clothes Retrieval dataset
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We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -an anchor point x is similar to a set of positive points Y , and dissimilar to a set of negative points Z, and a loss defined over these distances is minimized. While the specifics of the optimization differ, in this work we collectively call this type of supervision Triplets and all methods that follow this pattern Triplet-Based methods. These methods are challenging to optimize. A main issue is the need for finding informative triplets, which is usually achieved by a variety of tricks such as increasing the batch size, hard or semi-hard triplet mining, etc. Even with these tricks, the convergence rate of such methods is slow. In this paper we propose to optimize the triplet loss on a different space of triplets, consisting of an anchor data point and similar and dissimilar proxy points which are learned as well. These proxies approximate the original data points, so that a triplet loss over the proxies is a tight upper bound of the original loss. This proxy-based loss is empirically better behaved. As a result, the proxy-loss improves on state-of-art results for three standard zero-shot learning datasets, by up to 15% points, while converging three times as fast as other triplet-based losses.
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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.
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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。
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Learning the distance metric between pairs of examples is of great importance for learning and visual recognition. With the remarkable success from the state of the art convolutional neural networks, recent works [1, 31] have shown promising results on discriminatively training the networks to learn semantic feature embeddings where similar examples are mapped close to each other and dissimilar examples are mapped farther apart. In this paper, we describe an algorithm for taking full advantage of the training batches in the neural network training by lifting the vector of pairwise distances within the batch to the matrix of pairwise distances. This step enables the algorithm to learn the state of the art feature embedding by optimizing a novel structured prediction objective on the lifted problem. Additionally, we collected Online Products dataset: 120k images of 23k classes of online products for metric learning. Our experiments on the CUB-200-2011 [37], CARS196 [19], and Online Products datasets demonstrate significant improvement over existing deep feature embedding methods on all experimented embedding sizes with the GoogLeNet [33] network.
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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.
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在距离度量学习网络的培训期间,典型损耗函数的最小值可以被认为是满足由训练数据施加的一组约束的“可行点”。为此,我们将距离度量学习问题重构为查找约束集的可行点,其中训练数据的嵌入向量满足所需的类内和帧间接近度。由约束集引起的可行性集被表示为仅针对训练数据的特定样本(来自每个类别的样本)强制执行接近约束的宽松可行集合。然后,通过在那些可行的组上执行交替的投影来大致解决可行点问题。这种方法引入了正则化术语,并导致最小化具有系统批量组结构的典型损失函数,其中这些批次被约束以包含来自每个类的相同样本,用于一定数量的迭代。此外,这些特定样品可以被认为是阶级代表,允许在批量构建期间有效地利用艰难的挖掘。所提出的技术应用于良好的损失,并在斯坦福在线产品,CAR196和CUB200-2011数据集进行了评估,用于图像检索和聚类。表现优于现有技术,所提出的方法一致地提高了综合损失函数的性能,没有额外的计算成本,并通过硬负面挖掘进一步提高性能。
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在本文中,我们提出了一种强大的样本生成方案来构建信息性三联网。所提出的硬样品生成是一种两级合成框架,通过两个阶段的有效正和负样品发生器产生硬样品。第一阶段将锚定向对具有分段线性操作,通过巧妙地设计条件生成的对抗网络来提高产生的样本的质量,以降低模式崩溃的风险。第二阶段利用自适应反向度量约束来生成最终的硬样本。在几个基准数据集上进行广泛的实验,验证了我们的方法比现有的硬样生成算法达到卓越的性能。此外,我们还发现,我们建议的硬样品生成方法结合现有的三态挖掘策略可以进一步提高深度度量学习性能。
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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.
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最近,深度度量学习(DML)的实质性研究努力集中在设计复杂的成对距离损失,这需要卷积方案来缓解优化,例如样本挖掘或配对加权。分类的标准交叉熵损失在DML中大大忽略了。在表面上,交叉熵可能看起来不相关,与度量学习无关,因为它没有明确地涉及成对距离。但是,我们提供了一个理论分析,将交叉熵链接到几个众所周知的和最近的成对损耗。我们的连接是从两种不同的观点绘制:一个基于明确的优化洞察力;另一个关于标签与学到的相互信息的判别和生成观点。首先,我们明确证明交叉熵是新的成对损耗的上限,其具有类似于各种成对损耗的结构:它最大限度地减少了课堂内距离,同时最大化了阶级间距离。结果,最小化交叉熵可以被视为近似束缚 - 优化(或大大最小化)算法,以最小化该成对丢失。其次,我们表明,更一般地,最小化跨熵实际上是相当于最大化互联信息的相同信息,我们连接多个众所周知的成对损耗。此外,我们表明,各种标准成对损耗可以通过绑定的关系彼此明确地与彼此有关。我们的研究结果表明,交叉熵代表了最大化相互信息的代理 - 作为成对损耗,没有必要进行复杂的样品挖掘启发式。我们对四个标准DML基准测试的实验强烈支持我们的调查结果。我们获得最先进的结果,优于最近和复杂的DML方法。
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Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models. Modern batch contrastive approaches subsume or significantly outperform traditional contrastive losses such as triplet, max-margin and the N-pairs loss. In this work, we extend the self-supervised batch contrastive approach to the fully-supervised setting, allowing us to effectively leverage label information. Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes. We analyze two possible versions of the supervised contrastive (SupCon) loss, identifying the best-performing formulation of the loss. On ResNet-200, we achieve top-1 accuracy of 81.4% on the Ima-geNet dataset, which is 0.8% above the best number reported for this architecture. We show consistent outperformance over cross-entropy on other datasets and two ResNet variants. The loss shows benefits for robustness to natural corruptions, and is more stable to hyperparameter settings such as optimizers and data augmentations. Our loss function is simple to implement and reference TensorFlow code is released at https://t.ly/supcon 1 .
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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。
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Deep metric learning aims to learn an embedding space, where semantically similar samples are close together and dissimilar ones are repelled against. To explore more hard and informative training signals for augmentation and generalization, recent methods focus on generating synthetic samples to boost metric learning losses. However, these methods just use the deterministic and class-independent generations (e.g., simple linear interpolation), which only can cover the limited part of distribution spaces around original samples. They have overlooked the wide characteristic changes of different classes and can not model abundant intra-class variations for generations. Therefore, generated samples not only lack rich semantics within the certain class, but also might be noisy signals to disturb training. In this paper, we propose a novel intra-class adaptive augmentation (IAA) framework for deep metric learning. We reasonably estimate intra-class variations for every class and generate adaptive synthetic samples to support hard samples mining and boost metric learning losses. Further, for most datasets that have a few samples within the class, we propose the neighbor correction to revise the inaccurate estimations, according to our correlation discovery where similar classes generally have similar variation distributions. Extensive experiments on five benchmarks show our method significantly improves and outperforms the state-of-the-art methods on retrieval performances by 3%-6%. Our code is available at https://github.com/darkpromise98/IAA
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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。
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Despite significant recent advances in the field of face recognition [10,14,15,17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors.Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches. To train, we use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. The benefit of our approach is much greater representational efficiency: we achieve state-of-the-art face recognition performance using only 128-bytes per face.On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%. On YouTube Faces DB it achieves 95.12%. Our system cuts the error rate in comparison to the best published result [15] by 30% on both datasets.We also introduce the concept of harmonic embeddings, and a harmonic triplet loss, which describe different versions of face embeddings (produced by different networks) that are compatible to each other and allow for direct comparison between each other.
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Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances of the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by common metric learning losses. HIER achieved this goal with no annotation for the semantic hierarchy but by learning hierarchical proxies in hyperbolic spaces. The hierarchical proxies are learnable parameters, and each of them is trained to serve as an ancestor of a group of data or other proxies to approximate the semantic hierarchy among them. HIER deals with the proxies along with data in hyperbolic space since geometric properties of the space are well-suited to represent their hierarchical structure. The efficacy of HIER was evaluated on four standard benchmarks, where it consistently improved performance of conventional methods when integrated with them, and consequently achieved the best records, surpassing even the existing hyperbolic metric learning technique, in almost all settings.
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学习遥感(RS)图像之间的相似性形成基于内容的RS图像检索(CBIR)的基础。最近,将图像的语义相似性映射到嵌入(度量标准)空间的深度度量学习方法已经发现非常流行。学习公制空间的常见方法依赖于将与作为锚称为锚的参考图像的类似(正)和不同(负)图像的三胞胎的选择。选择三胞胎是一个难以为多标签RS CBIR的困难任务,其中每个训练图像由多个类标签注释。为了解决这个问题,在本文中,我们提出了一种在为多标签RS CBIR问题定义的深神经网络(DNN)的框架中提出了一种新颖的三联样品采样方法。该方法基于两个主要步骤选择一小部分最多代表性和信息性三元组。在第一步中,使用迭代算法从当前迷你批量选择在嵌入空间中彼此多样化的一组锚。在第二步中,通过基于新颖的策略评估彼此之间的图像的相关性,硬度和多样性来选择不同的正面和负图像。在两个多标签基准档案上获得的实验结果表明,在DNN的上下文中选择最具信息丰富和代表性的三胞胎,导致:i)降低DNN训练阶段的计算复杂性,而性能没有任何显着损失; ii)由于信息性三元组允许快速收敛,因此学习速度的增加。所提出的方法的代码在https://git.tu-berlin.de/rsim/image-reetrieval-from-tropls上公开使用。
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对比损失长期以来一直是深度度量学习的关键成分,现在由于自我监督学习的成功而正在变得越来越受欢迎。最近的研究表明,在学习代表网络时以互补的方式分解这种损失的损失:正期和熵项。虽然因此整体损失被定义为两种术语的组合,但这两个术语的余额通常隐藏在实施细节之后,并且在实践中很大程度上被忽略和次优。在这项工作中,我们将对比损失的平衡作为超参数优化问题,并提出了一种基于坐标的下降的搜索方法,可有效地找到优化评估性能的超参数。在此过程中,我们将现有的余额分析扩展到对比度边缘损失,包括批次大小在余额中,并解释如何从批处理中汇总损耗元素,以在更大范围内保持近最佳性能。来自深度度量学习和自我监督学习的基准的广泛实验表明,使用我们的方法比其他常用搜索方法更快地找到最佳超参数。
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In recent years, deep metric learning and its probabilistic extensions claimed state-of-the-art results in the face verification task. Despite improvements in face verification, probabilistic methods received little attention in the research community and practical applications. In this paper, we, for the first time, perform an in-depth analysis of known probabilistic methods in verification and retrieval tasks. We study different design choices and propose a simple extension, achieving new state-of-the-art results among probabilistic methods. Finally, we study confidence prediction and show that it correlates with data quality, but contains little information about prediction error probability. We thus provide a new confidence evaluation benchmark and establish a baseline for future confidence prediction research. PyTorch implementation is publicly released.
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