mlsanityData to 14 Oct 2026

Deep Convolutional Ranking for Multilabel Image Annotation

arXiv 1312.4894 ↗First posted 17 Dec 2013cs.CVcited by 47

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Abstract (arXiv)

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.

Key facts. Cited by 47 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Dec 2013.

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Citing papers per year: 2021: 14; 2022: 7; 2023: 7; 2024: 8; 2025: 10; 2026: 1.

47 papers.

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  29. 2022ProjB: An Improved Bilinear Biased ProjE model for Knowledge Graph Completioncited by 0
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