A Deep Model for Partial Multi-label Image Classification with Curriculum-based Disambiguation  

作　　者：Feng Sun Ming-Kun Xie Sheng-Jun Huang 

机构地区：[1]MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,College of Computer Science and Technology,Nanjing University of AeronauticsandAstronautics,Nanjing211106,China

出　　处：《Machine Intelligence Research》2024年第4期801-814,共14页机器智能研究（英文版）

摘　　要：In this paper,we study the partial multi-label(PML)image classification problem,where each image is annotated with a candidate label set consisting of multiple relevant labels and other noisy labels.Existing PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions,which unfortunately is unavailable in many real tasks.Furthermore,because the objective function for disambiguation is usually elaborately designed on the whole training set,it can hardly be optimized in a deep model with stochastic gradient descent(SGD)on mini-batches.In this paper,for the first time,we propose a deep model for PML to enhance the representation and discrimination ability.On the one hand,we propose a novel curriculum-based disambiguation strategy to progressively identify ground-truth labels by incorporating the varied difficulties of different classes.On the other hand,consistency regularization is introduced for model training to balance fitting identified easy labels and exploiting potential relevant labels.Extensive experimental results on the commonly used benchmark datasets show that the proposed method significantlyoutperforms the SOTA methods.

关 键 词：Partial multi-label image classification curriculum-based disambiguation consistency regularization label difficulty candidatelabel set. 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] R776.1[自动化与计算机技术—控制科学与工程]