The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
CNN-RNN: A Unified Framework for Multi-label Image Classification
Abstract
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image. Traditional approaches to multi-label image classification learn independent classifiers for each category and employ ranking or thresholding on the classification results. These techniques, although working well, fail to explicitly exploit the label dependencies in an image. In this paper, we utilize recurrent neural networks (RNNs) to address this problem. Combined with CNNs, the proposed CNN-RNN framework learns a joint image-label embedding to characterize the semantic label dependency as well as the image-label relevance, and it can be trained end-to-end from scratch to integrate both information in a unified framework. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves better performance than the state-of-the-art multi-label classification models.
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Cites
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Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019) · Computational Social Networks · doi:10.1186/s40649-019-0069-y · Cited by 1,888
Neural Motifs: Scene Graph Parsing with Global Context
Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00611 · Cited by 1,052
Pathology image analysis using segmentation deep learning algorithms
Wang, S., Yang, D. M., Rong, R., Zhan, X., & Xiao, G. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.05.007 · Cited by 382
Multimodal MR Synthesis via Modality-Invariant Latent Representation
Chartsias, A., Joyce, T., Giuffrida, M. V., & Tsaftaris, S. A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2764326 · Cited by 341
Metadata
- Authors
- Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., & Xu, W.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 2285-2294
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963745697
- Cited by
- 1,198
- References
- 9
- FWCI
- 34.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., & Xu, W. (2016). CNN-RNN: A Unified Framework for Multi-label Image Classification. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2016.251
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