ML-GCN embeds nodes via GCN, generates a label matrix in the same space, and trains with relaxed skip-gram on node-label concatenations to model correlations, reporting outperformance on graph datasets.
Deep Convolutional Ranking for Multilabel Image Annotation
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual features for multilabel annotation, features based on Deep Neural Networks have shown potential to significantly boost performance. In this work, we propose to leverage the advantage of such features and analyze key components that lead to better performances. Specifically, we show that a significant performance gain could be obtained by combining convolutional architectures with approximate top-$k$ ranking objectives, as thye naturally fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset outperforms the conventional visual features by about 10%, obtaining the best reported performance in the literature.
fields
cs.LG 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
Multi-label neural networks with optimal thresholding outperform binary relevance PLS-DA on synthetic IR spectra for multi-gas identification when SNR and training sample size are sufficient.
citing papers explorer
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Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification
ML-GCN embeds nodes via GCN, generates a label matrix in the same space, and trains with relaxed skip-gram on node-label concatenations to model correlations, reporting outperformance on graph datasets.
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Multi-label Classification with Optimal Thresholding for Multi-composition Spectroscopic Analysis
Multi-label neural networks with optimal thresholding outperform binary relevance PLS-DA on synthetic IR spectra for multi-gas identification when SNR and training sample size are sufficient.