{"title":"Adaptive ambiguity-aware weighting for multi-label recognition with limited annotations","authors":"","doi":"10.1016/j.neunet.2024.106642","DOIUrl":null,"url":null,"abstract":"<div><p>In multi-label recognition, effectively addressing the challenge of partial labels is crucial for reducing annotation costs and enhancing model generalization. Existing methods exhibit limitations by relying on unrealistic simulations with uniformly dropped labels, overlooking how ambiguous instances and instance-level factors impacts label ambiguity in real-world datasets. To address this deficiency, our paper introduces a realistic partial label setting grounded in instance ambiguity, complemented by Reliable Ambiguity-Aware Instance Weighting (R-AAIW)—a strategy that utilizes importance weighting to adapt dynamically to the inherent ambiguity of multi-label instances. The strategy leverages an ambiguity score to prioritize learning from clearer instances. As proficiency of the model improves, the weights are dynamically modulated to gradually shift focus towards more ambiguous instances. By employing an adaptive re-weighting method that adjusts to the complexity of each instance, our approach not only enhances the model’s capability to detect subtle variations among labels but also ensures comprehensive learning without excluding difficult instances. Extensive experimentation across various benchmarks highlights our approach’s superiority over existing methods, showcasing its ability to provide a more accurate and adaptable framework for multi-label recognition tasks.</p></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608024005665","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In multi-label recognition, effectively addressing the challenge of partial labels is crucial for reducing annotation costs and enhancing model generalization. Existing methods exhibit limitations by relying on unrealistic simulations with uniformly dropped labels, overlooking how ambiguous instances and instance-level factors impacts label ambiguity in real-world datasets. To address this deficiency, our paper introduces a realistic partial label setting grounded in instance ambiguity, complemented by Reliable Ambiguity-Aware Instance Weighting (R-AAIW)—a strategy that utilizes importance weighting to adapt dynamically to the inherent ambiguity of multi-label instances. The strategy leverages an ambiguity score to prioritize learning from clearer instances. As proficiency of the model improves, the weights are dynamically modulated to gradually shift focus towards more ambiguous instances. By employing an adaptive re-weighting method that adjusts to the complexity of each instance, our approach not only enhances the model’s capability to detect subtle variations among labels but also ensures comprehensive learning without excluding difficult instances. Extensive experimentation across various benchmarks highlights our approach’s superiority over existing methods, showcasing its ability to provide a more accurate and adaptable framework for multi-label recognition tasks.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.