{"title":"广义零点学习的属性反纠缠和再纠缠","authors":"","doi":"10.1016/j.patrec.2024.09.007","DOIUrl":null,"url":null,"abstract":"<div><p>The key challenge in zero-shot learning is inferring latent semantic knowledge between visual and attribute features of seen classes to achieve knowledge transfer to unseen classes. To address the limitation that local attribute features can only ensure attribute-level recognition rather than classification of an entire class, some methods incorporate global information into the process or results of local features extraction for classification. However, these approaches have not effectively addressed the issue. To address these issues, we propose an Attribute Disentanglement and Re-entanglement for Generalized Zero-Shot Learning. Our model no longer implicitly or explicitly incorporates global information into local attribute features for classification. Instead, we adjust local attribute features to make them more suitable for classification in re-entanglement phase, while ensuring the correct extraction of these features in disentanglement phase. We employ appropriate optimization loss functions and achieve significant improvements on three challenging benchmark datasets. Compared to other similar methods, our model exhibits strong competitiveness.</p></div>","PeriodicalId":54638,"journal":{"name":"Pattern Recognition Letters","volume":null,"pages":null},"PeriodicalIF":3.9000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Attribute disentanglement and re-entanglement for generalized zero-shot learning\",\"authors\":\"\",\"doi\":\"10.1016/j.patrec.2024.09.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The key challenge in zero-shot learning is inferring latent semantic knowledge between visual and attribute features of seen classes to achieve knowledge transfer to unseen classes. To address the limitation that local attribute features can only ensure attribute-level recognition rather than classification of an entire class, some methods incorporate global information into the process or results of local features extraction for classification. However, these approaches have not effectively addressed the issue. To address these issues, we propose an Attribute Disentanglement and Re-entanglement for Generalized Zero-Shot Learning. Our model no longer implicitly or explicitly incorporates global information into local attribute features for classification. Instead, we adjust local attribute features to make them more suitable for classification in re-entanglement phase, while ensuring the correct extraction of these features in disentanglement phase. We employ appropriate optimization loss functions and achieve significant improvements on three challenging benchmark datasets. Compared to other similar methods, our model exhibits strong competitiveness.</p></div>\",\"PeriodicalId\":54638,\"journal\":{\"name\":\"Pattern Recognition Letters\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pattern Recognition Letters\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167865524002691\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167865524002691","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
零镜头学习的关键挑战在于推断已见类别的视觉和属性特征之间的潜在语义知识,从而实现向未见类别的知识转移。为了解决局部属性特征只能确保属性级识别而不能确保整个类别分类的局限性,一些方法将全局信息纳入局部特征提取的过程或结果中,以进行分类。然而,这些方法并未有效解决这一问题。为了解决这些问题,我们提出了 "广义零点学习的属性分解和重新分解"(Attribute Disentanglement and Re-entanglement for Generalized Zero-Shot Learning)。我们的模型不再隐式或显式地将全局信息纳入本地属性特征进行分类。相反,我们会调整局部属性特征,使其更适合在重新纠缠阶段进行分类,同时确保在解除纠缠阶段正确提取这些特征。我们采用了适当的优化损失函数,并在三个具有挑战性的基准数据集上取得了显著的改进。与其他类似方法相比,我们的模型具有很强的竞争力。
Attribute disentanglement and re-entanglement for generalized zero-shot learning
The key challenge in zero-shot learning is inferring latent semantic knowledge between visual and attribute features of seen classes to achieve knowledge transfer to unseen classes. To address the limitation that local attribute features can only ensure attribute-level recognition rather than classification of an entire class, some methods incorporate global information into the process or results of local features extraction for classification. However, these approaches have not effectively addressed the issue. To address these issues, we propose an Attribute Disentanglement and Re-entanglement for Generalized Zero-Shot Learning. Our model no longer implicitly or explicitly incorporates global information into local attribute features for classification. Instead, we adjust local attribute features to make them more suitable for classification in re-entanglement phase, while ensuring the correct extraction of these features in disentanglement phase. We employ appropriate optimization loss functions and achieve significant improvements on three challenging benchmark datasets. Compared to other similar methods, our model exhibits strong competitiveness.
期刊介绍:
Pattern Recognition Letters aims at rapid publication of concise articles of a broad interest in pattern recognition.
Subject areas include all the current fields of interest represented by the Technical Committees of the International Association of Pattern Recognition, and other developing themes involving learning and recognition.