As an emerging topic in face recognition, designing margin-based loss functions can increase the feature margin between different classes for enhanced discriminability. More recently, the idea of mining-based strategies is adopted to emphasize the misclassified samples, achieving promising results. However, during the entire training process, the prior methods either do not explicitly emphasize the sample based on its importance that renders the hard samples not fully exploited; or explicitly emphasize the effects of semi-hard/hard samples even at the early training stage that may lead to convergence issue. In this work, we propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function to achieve a novel training strategy for deep face recognition, which mainly addresses easy samples in the early training stage and hard ones in the later stage. Specifically, our CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages. In each stage, different samples are assigned with different importance according to their corresponding difficultness. Extensive experimental results on popular benchmarks demonstrate the superiority of our CurricularFace over the state-of-the-art competitors.
AdaSin: Enhancing Hard Sample Metrics with Dual Adaptive Penalty for Face Recognition
School of Intelligent Systems Science and Engineering, Jinan University（暨南大学智能系统科学与工程学院）；College of Cyber Security, Jinan University（暨南大学网络空间安全学院）；School of Computer Science and Engineering, Macau University of Science and Technology（澳门科技大学计算机科学与工程学院）
University of Ljubljana, Faculty of Electrical Engineering（卢布尔雅那大学电气工程学院）；University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学计算机与信息科学学院）
Recent state-of-the-art face recognition (FR) approaches have achieved impressive performance, yet unconstrained face recognition still represents an open problem. Face image quality assessment (FIQA) approaches aim to estimate the quality of the input samples that can help provide information on the confidence of the recognition decision and eventually lead to improved results in challenging scenarios. While much progress has been made in face image quality assessment in recent years, computing reliable quality scores for diverse facial images and FR models remains challenging. In this paper,…
Monash University（莫纳什大学）；Tencent AI Lab（腾讯AI Lab）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Face recognition has been greatly facilitated by the development of deep neural networks (DNNs) and has been widely applied to many safety-critical applications. However, recent studies have shown that DNNs are very vulnerable to adversarial examples, raising serious concerns on the security of real-world face recognition. In this work, we study sticker-based physical attacks on face recognition for better understanding its adversarial robustness. To this end, we first analyze in-depth the complicated physical-world conditions confronted by attacking face recognition, including the different v…
NEC Corporation（日本电气股份有限公司）；University of Tsukuba（筑波大学）
This paper introduces a novel adversarial example generation method against face recognition systems (FRSs). An adversarial example (AX) is an image with deliberately crafted noise to cause incorrect predictions by a target system. The AXs generated from our method remain robust under real-world brightness changes. Our method performs non-linear brightness transformations while leveraging the concept of curriculum learning during the attack generation procedure. We demonstrate that our method outperforms conventional techniques from comprehensive experimental investigations in the digital and …
A Multi-task Adversarial Attack Against Face Authentication
National Institute of Informatics（信息学研究所）；Shanghai Jiao Tong University（上海交通大学）；Monash University（莫纳什大学）；University of Sassari（萨萨里大学）；Anhui University（安徽大学）
ErasableMask: A Robust and Erasable Privacy Protection Scheme against Black-box Face Recognition Models
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University（航空航天信息安全与可信计算重点实验室，教育部，武汉大学计算机科学与工程学院）；School of Mathematics and Statistics, South-Central University for Nationalities（数学与统计学学院，中南民族大学）；Cyberspace Institute of Advanced Technology, Guangzhou University（先进技术网络研究院，广州大学）
Human attribute analysis is a challenging task in the field of computer vision, since the data is largely imbalance-distributed. Common techniques such as re-sampling and cost-sensitive learning require prior-knowledge to train the system. To address this problem, we propose a unified framework called Dynamic Curriculum Learning (DCL) to online adaptively adjust the sampling strategy and loss learning in single batch, which resulting in better generalization and discrimination. Inspired by the curriculum learning, DCL consists of two level curriculum schedulers: (1) sampling scheduler not only…
University of Southern California（南加州大学）；Google, Inc.（谷歌公司）
When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects. To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects. We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework. The MICL method starts…
Hybrid Generative Fusion for Efficient and Privacy-Preserving Face Recognition Dataset Generation
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；BDKM, University of Chinese Academy of Sciences（中国科学院大学BDKM）
Xiaomi AI Lab（小米人工智能实验室）；Chinese University of Hong Kong（香港中文大学）；Peking University（北京大学）
Training generative models like Generative Adversarial Network (GAN) is challenging for noisy data. A novel curriculum learning algorithm pertaining to clustering is proposed to address this issue in this paper. The curriculum construction is based on the centrality of underlying clusters in data points. The data points of high centrality takes priority of being fed into generative models during training. To make our algorithm scalable to large-scale data, the active set is devised, in the sense that every round of training proceeds only on an active subset containing a small fraction of alrea…
Scale-Aware Curriculum Learning for Ddata-Efficient Lung Nodule Detection with YOLOv11
Department of Biomedical Engineering, Johns Hopkins University（生物医学工程系，约翰霍普金斯大学）；Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University（放射肿瘤学与分子放射科学系，约翰霍普金斯大学）
Federated Distillation for Whole Slide Image via Gaussian-Mixture Feature Alignment and Curriculum Integration
School of Computer Science, Peking University, Beijing, China（北京大学计算机科学系）；Center for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2113, Australia（健康信息学中心，澳大利亚健康创新研究所，麦考利大学，悉尼，NSW 2113，澳大利亚）；School of Data Science, University of Virginia, Charlottesville, VA, USA（数据科学学院，弗吉尼亚大学，夏洛特维尔，VA，美国）
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
Australian Institute for Machine Learning, University of Adelaide, Australia（澳大利亚机器学习研究所，阿德莱德大学，澳大利亚）；Department of Data Science and AI, Monash University, Australia（数据科学与人工智能系，莫纳什大学，澳大利亚）；Centre for Vision, Speech and Signal Processing, University of Surrey, UK（视觉、语音与信号处理中心，萨里大学，英国）
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing（测绘遥感信息工程国家重点实验室）；Wuhan University（武汉大学）；Information Systems Technology and Design Pillar（信息系统技术与设计学院）；Singapore University of Technology and Design（新加坡科技与设计大学）；School of Geodesy and Geomatics（测绘学院）；School of Mathematics and Statistics（数学与统计学院）；Wuhan University Shenzhen Research Institute（武汉大学深圳研究院）
Sun Yat-sen University（中山大学）；Harbin Institute of Technology（哈尔滨工业大学）；The Hong Kong Polytechnic University（香港理工大学）
This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert re-certification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a…
