ArcFace: Additive Angular Margin Loss for Deep Face Recognition
15 Jun 2019-pp 4690-4699
TL;DR: This paper presents arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks, and shows that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead.
Abstract: One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that can enhance the discriminative power. Centre loss penalises the distance between deep features and their corresponding class centres in the Euclidean space to achieve intra-class compactness. SphereFace assumes that the linear transformation matrix in the last fully connected layer can be used as a representation of the class centres in the angular space and therefore penalises the angles between deep features and their corresponding weights in a multiplicative way. Recently, a popular line of research is to incorporate margins in well-established loss functions in order to maximise face class separability. In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to its exact correspondence to geodesic distance on a hypersphere. We present arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks which includes a new large-scale image database with trillions of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead. To facilitate future research, the code has been made available.
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01 Jun 2022
TL;DR: In this paper , a deformable prototypical part network (Deformable ProtoPNet) is proposed to combine the power of deep learning and the interpretability of case-based reasoning.
Abstract: We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning. This model classifies input images by comparing them with prototypes learned during training, yielding explanations in the form of “this looks like that.” However, while previous methods use spatially rigid prototypes, we address this shortcoming by proposing spatially flexible prototypes. Each prototype is made up of several prototypical parts that adaptively change their relative spatial positions depending on the input image. Consequently, a Deformable ProtoPNet can explicitly capture pose variations and context, improving both model accuracy and the richness of explanations provided. Compared to other case-based interpretable models using prototypes, our approach achieves state-of-the-art accuracy and gives an explanation with greater context. The code is available at https://github.com/jdonnelly36IDeformable-ProtoPNet.
6 citations
TL;DR: A comprehensive review of deep micro-expression recognition based on deep learning can be found in this paper , including datasets, each step of the deep ML pipeline, and performance comparisons of the most influential methods.
Abstract: Micro-expressions (MEs) are involuntary facial movements revealing people's hidden feelings in high-stake situations and have practical importance in various fields. Early methods for Micro-expression Recognition (MER) are mainly based on traditional features. Recently, with the success of Deep Learning (DL) in various tasks, neural networks have received increasing interest in MER. Different from macro-expressions, MEs are spontaneous, subtle, and rapid facial movements, leading to difficult data collection and annotation, thus publicly available datasets are usually small-scale. Currently, various DL approaches have been proposed to solve the ME issues and improve MER performance. In this survey, we provide a comprehensive review of deep MER and define a new taxonomy for the field encompassing all aspects of MER based on DL, including datasets, each step of the deep MER pipeline, and performance comparisons of the most influential methods. The basic approaches and advanced developments are summarized and discussed for each aspect. Additionally, we conclude the remaining challenges and potential directions for the design of robust MER systems. Finally, ethical considerations in MER are discussed. To the best of our knowledge, this is the first survey of deep MER methods, and this survey can serve as a reference point for future MER research.
6 citations
01 Jun 2022
TL;DR: GP-UNIT as mentioned in this paper distill the generative prior to capture a robust coarse-level content representation that can link objects at an abstract semantic level, based on which finelevel content features are adaptively learned for more accurate multi-level correspondences.
Abstract: Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. Despite the recent progress in image translation models, it remains challenging to build mappings between complex domains with drastic visual discrepancies. In this work, we present a novel framework, Generative Priorguided UNsupervised Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm. Our key insight is to leverage the generative prior from pre-trained class-conditional GANs (e.g., BigGAN) to learn rich content correspondences across various domains. We propose a novel coarse-to-fine scheme: we first distill the generative prior to capture a robust coarse-level content representation that can link objects at an abstract semantic level, based on which finelevel content features are adaptively learned for more accurate multi-level content correspondences. Extensive experiments demonstrate the superiority of our versatile framework over state-of-the-art methods in robust, high-quality and diversified translations, even for challenging and distant domains. Code is available at https://github.com/williamyang1991/GP-UNIT.
6 citations
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TL;DR: AdaSample, an adaptive online batch sampler, is proposed, where hard positives are sampled based on their informativeness and a hardness-aware positive mining pipeline is formulated within a novel maximum loss minimization training protocol.
Abstract: Triplet loss has been widely employed in a wide range of computer vision tasks, including local descriptor learning. The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets. For high-informativeness triplet collection, researchers mostly focus on mining hard negatives in the second stage, while paying relatively less attention to constructing informative batches. To alleviate this issue, we propose AdaSample, an adaptive online batch sampler, in this paper. Specifically, hard positives are sampled based on their informativeness. In this way, we formulate a hardness-aware positive mining pipeline within a novel maximum loss minimization training protocol. The efficacy of the proposed method is evaluated on several standard benchmarks, where it demonstrates a significant and consistent performance gain on top of the existing strong baselines.
6 citations
Cites methods from "ArcFace: Additive Angular Margin Lo..."
...Therefore, we adopt the angular distance [7] as follows:...
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TL;DR: This work proposes a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image to alleviate batch effects and introduces the Additive Angular Margin Loss to enhance the discriminative power of the model.
Abstract: Chromosome classification is an important but difficult and tedious task in karyotyping Previous methods only classify manually segmented single chromosome, which is far from clinical practice In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image We firstly introduce the Additive Angular Margin Loss to enhance the discriminative power of model To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of prior knowledges that chromosomes usually appear in pairs Furthermore, we take the clinically seven group criterion as a prior knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities 3390 metaphase images from clinical laboratory are collected and labelled to evaluate the performance Results show that the new design brings encouraging performance gains comparing to the state-of-the-art baselines
6 citations
Cites background or methods from "ArcFace: Additive Angular Margin Lo..."
...We firstly introduce the Additive Angular Margin Loss [11] to enforce higher intra-class compactness and inter-class discrepancy of the model simultaneously....
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...Some works [11, 16, 17] try to achieve above requirements by incorporating margins in an established loss function....
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...Inspired by the visual object tracking, the classification branch is adopted by a siamese architecture which consists two streams sharing two 1024×1024 fully connected layers, the first stream named margin branch optimized by Additive Angular Margin Loss [11] (Section 2....
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...Inspired by recent research about loss functions of face recognition, we replace the original Cross Entropy (CE) Loss with Additive Angular Margin (AAM) Loss [11]....
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Posted Content•
TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
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