Research On Image Recognition Algorithms For Real-Word Long-Tail Data

In recent years,image recognition technology has achieved significant breakthroughs and wide-ranging applications in various fields,such as facial recognition.However,when applied to industrial domains,these technologies still face numerous challenges with real-world data,such as long-tailed distributions,noisy labels,domain shifts,and open-set recognition.These issues impact the effectiveness of models in industrial applications.This study primarily focuses on addressing the problems of long-tailed distributions and noisy labels in models.Long-tailed distribution data refers to real-world data where common classes have abundant samples while other classes are extremely scarce.This imbalance causes models to struggle in learning effective representations from the rare tail data,affecting their accuracy and generalization during prediction.Additionally,as dataset sizes increase,maintaining annotation quality becomes harder,leading to noisy labels.These noisy labels mislead models into learning incorrect patterns,thereby affecting their accuracy and generalization ability.When these two issues occur simultaneously,the challenges for the models become even more severe.Against this backdrop,this research delves into the impact of noisy labels and long-tailed distributions on deep neural networks and proposes innovative algorithms to address these issues:(1)For long-tailed image data,a recognition algorithm based on diverse experts and consistency self-distillation is proposed.This algorithm designs a new diversity learning loss function to enhance the model’s recognition diversity across different classes,thus improving overall recognition performance.Additionally,a consistency selfdistillation module is introduced.This module uses the distilled knowledge from weakly augmented instances as supervision,providing richer class information for each sample,thereby reducing overfitting on tail classes and enhancing model generalization.Extensive comparative experiments validate that the proposed algorithm outperforms existing methods by approximately 1-2 percentage points on long-tailed benchmarks such as CIFAR-10/100,Place-LT,Image Net-LT,and i Nauralist2018.(2)For noisy label image data,a recognition algorithm based on probability difference modeling is proposed.This algorithm fully exploits the output information between classes,introducing a sample modeling method based on probability differences that effectively identifies and distinguishes noisy label samples from hard samples,allowing the model to continue learning clean sample patterns in subsequent training.Furthermore,traditional algorithms overly rely on noise rate priors,which are often unavailable in real-world data.Addressing this issue,the algorithm proposes a new learning paradigm based on global distribution,independent of noise rate.To avoid the waste of training data information caused by discarding noisy label samples directly,a new loss function is designed to extract useful information from noisy label data.Comparative experiments and ablation studies on synthetic datasets CIFAR-10/100 and real datasets Clothing1 M,Web Vision demonstrate that the proposed algorithm outperforms state-of-the-art methods by approximately 1-3percentage points,confirming its effectiveness.(3)For noisy label long-tailed image data,an online class-aware noise-robust long-tailed image recognition algorithm is proposed.This algorithm addresses the challenge of extracting tail class features when both long-tailed distribution and noisy labels are present.It introduces a new training strategy and dynamic rebalancing loss to filter noisy label data and rebalance learned features for each class.Extensive comparative experiments and ablation studies show that the proposed algorithm mitigates the impact of noisy labels during training and leads in performance over existing methods by approximately 1-2 percentage points on benchmark datasets,proving its effectiveness.(4)For remote sensing data,characterized by complex features,data diversity,and high resolution,a new backbone network,Vision Transformer,is introduced,along with an innovative data augmentation and resampling algorithm to assist the previously proposed algorithms in training the model.A series of comparative experiments and ablation studies demonstrate the effectiveness of the proposed algorithm in handling remote sensing data,significantly outperforming existing methods by about 1 percentage point.These experimental results not only validate the practicality of the proposed algorithms but also provide new references for solving long-tailed distribution and noisy label image recognition issues in other fields.