Results 11 to 20 of about 881,675 (221)
Classifier Chains for Multi-label Classification [PDF]
Machine Learning, 2009 The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels.
Read, Jesse +3 more
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DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification [PDF]
AAAI Conference on Artificial Intelligence, 2023 In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection and manual annotation, which means ...
Chengliang Liu +5 more
semanticscholar +1 more source
Asymmetric Loss For Multi-Label Classification [PDF]
IEEE International Conference on Computer Vision, 2020 In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones. This positive-negative imbalance dominates the optimization process, and can lead to under-emphasizing gradients from positive labels during ...
Emanuel Ben Baruch +6 more
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Large Loss Matters in Weakly Supervised Multi-Label Classification [PDF]
Computer Vision and Pattern Recognition, 2022 Weakly supervised multi-label classification (WSML) task, which is to learn a multi-label classification using partially observed labels per image, is becoming increasingly important due to its huge annotation cost.
Youngwook Kim +3 more
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Comprehensive Comparative Study of Multi-Label Classification Methods [PDF]
Expert systems with applications, 2021 Multi-label classification (MLC) has recently received increasing interest from the machine learning community. Several studies provide reviews of methods and datasets for MLC and a few provide empirical comparisons of MLC methods.
Jasmin Bogatinovski +3 more
semanticscholar +1 more source
An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text [PDF]
Annual Meeting of the Association for Computational Linguistics, 2023 Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks. In this study, we compare four methods for multi-label
Yova Kementchedjhieva, Ilias Chalkidis
semanticscholar +1 more source
Unsupervised Person Re-Identification via Multi-Label Classification [PDF]
Computer Vision and Pattern Recognition, 2020 The challenge of unsupervised person re-identification (ReID) lies in learning discriminative features without true labels. This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels.
Dongkai Wang, Shiliang Zhang
semanticscholar +1 more source
Open-Vocabulary Multi-Label Classification via Multi-modal Knowledge Transfer [PDF]
AAAI Conference on Artificial Intelligence, 2022 Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embedding (e.g., GloVe). However, such
Su He +5 more
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ZLPR: A Novel Loss for Multi-label Classification [PDF]
arXiv.org, 2022 In the era of deep learning, loss functions determine the range of tasks available to models and algorithms. To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zero-bounded log-sum-exp \&pairwise ...
Jianlin Su +5 more
semanticscholar +1 more source
SSC-Net: A multi-task joint learning network for tongue image segmentation and multi-label classification. [PDF]
Digit HealthBackground Traditional Chinese medicine (TCM) tongue diagnosis, through the comprehensive observation of tongue’s diverse characteristics, allows an understanding of the state of the body’s viscera as well as Qi and blood levels.
Sha X +5 more
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