Acquiring accurate labels on large-scale datasets is both time consuming and expensive. To reduce the dependency of deep learning models on learning from clean labeled data, several recent research efforts are focused on learning with noisy labels. These methods typically fall into three design categories to learn a noise robust model: sample selection approaches, noise robust loss functions, or label correction methods. In this paper, we propose PARS: Pseudo-Label Aware Robust Sample Selection, a hybrid approach that combines the best from all three worlds in a joint-training framework to achieve robustness to noisy labels. Specifically, PARS exploits all training samples using both the raw/noisy labels and estimated/refurbished pseudo-labels via self-training, divides samples into an ambiguous and a noisy subset via loss analysis, and designs label-dependent noise-aware loss functions for both sets of filtered labels. Results show that PARS significantly outperforms the state of the art on extensive studies on the noisy CIFAR-10 and CIFAR-100 datasets, particularly on challenging high-noise and low-resource settings. In particular, PARS achieved an absolute 12% improvement in test accuracy on the CIFAR-100 dataset with 90% symmetric label noise, and an absolute 27% improvement in test accuracy when only 1/5 of the noisy labels are available during training as an additional restriction. On a real-world noisy dataset, Clothing1M, PARS achieves competitive results to the state of the art.
Advancing Weakly-Supervised Audio-Visual Video Parsing via Segment-wise Pseudo Labeling
Hefei University of Technology（合肥工业大学）；Shanghai AI Laboratory（上海人工智能实验室）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Anhui Zhonghuitong Technology Co., Ltd.（安徽中汇通科技有限公司）
EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications（智能工程与自动化学院，北京邮电大学）；State Grid Corporation of China（国家电网公司）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）
Robust Multi-Modal Face Anti-Spoofing with Domain Adaptation: Tackling Missing Modalities, Noisy Pseudo-Labels, and Model Degradation
College of Computer and Cyber Security, Fujian Normal University（计算机与网络安全部，福建师范大学）；Department of Computer Science, National Tsing Hua University（计算机科学系，国立清华大学）
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
The Hong Kong University of Science and Technology（香港科技大学）；SenseTime Research（商汤科技研究院）；University of Technology Sydney（悉尼科技大学）；The Hong Kong University of Science and Technology Shenzhen Research Institute（香港科技大学深圳研究院）
Robust Pseudo-label Learning with Neighbor Relation for Unsupervised Visible-Infrared Person Re-Identification
School of Informatics, Xiamen University（厦门大学信息学院）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；East China Normal University（华东师范大学）
Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）
Learning Camouflaged Object Detection from Noisy Pseudo Label
Beijing Institute of Technology（北京理工大学）；Shanghai Institute of Technology（上海应用技术大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Linköping University（林雪平大学）
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation
Shanghai AI Laboratory（上海人工智能实验室）；Loughborough University（拉夫堡大学）；Northwestern Polytechnical University（西北工业大学）；CUHK-Shenzhen（香港中文大学（深圳））；Institute of Artificial Intelligence (TeleAI)（人工智能研究所（腾讯人工智能实验室））
Decoupled Pseudo-labeling for Semi-Supervised Monocular 3D Object Detection
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Baidu Inc.（百度公司）；GuangDong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing
Graduate Institute of Communication Engineering, National Taiwan University（国立台湾大学通信工程研究所）；NVIDIA, Taiwan（台湾NVIDIA公司）；Mitsubishi Electric Research Labs (MERL)（三菱电机研究实验室）
Instance-dependent Label Distribution Estimation for Learning with Label Noise
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Australian Insititute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）
TEn-CATG:Text-Enriched Audio-Visual Video Parsing with Multi-Scale Category-Aware Temporal Graph
Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（视觉、语音和信号处理中心（CVSSP），萨里大学）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（计算机科学与人工智能学院，武汉理工大学）；National Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（通用人工智能国家重点实验室，北京大学智能科学与技术学院）；College of Information and Electrical Engineering, China Agricultural University（信息与电子工程学院，中国农业大学）
Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学部生物医学工程学院）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州研究院）；Army Medical University (Third Military Medical University)（中国人民解放军陆军军医大学（第三军医大学））；The First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；University of Science and Technology of China（中国科学技术大学）
Bridging Weakly-Supervised Learning and VLM Distillation: Noisy Partial Label Learning for Efficient Downstream Adaptation
Institute of Perceptual Intelligence, Peng Cheng Laboratory（感知智能研究院，鹏城实验室）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）
