The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Multi-label Image Recognition by Recurrently Discovering Attentional Regions
Open accessAbstract
This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding. Current solutions for this task usually rely on an extra step of extracting hypothesis regions (i.e., region proposals), resulting in redundant computation and sub-optimal performance. In this work, we achieve the interpretable and contextualized multi-label image classification by developing a recurrent memorized-attention module. This module consists of two alternately performed components: i) a spatial transformer layer to locate attentional regions from the convolutional feature maps in a region-proposal-free way and ii) an LSTM (Long-Short Term Memory) sub-network to sequentially predict semantic labeling scores on the located regions while capturing the global dependencies of these regions. The LSTM also output the parameters for computing the spatial transformer. On large-scale benchmarks of multi-label image classification (e.g., MS-COCO and PASCAL VOC 07), our approach demonstrates superior performances over other existing state-of-the-arts in both accuracy and efficiency.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
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ImageNet Large Scale Visual Recognition Challenge
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Cited by
Ge, W., Lin, X., & Yu, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00315 · Cited by 317
Attention, please! A survey of neural attention models in deep learning
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The classification of construction waste material using a deep convolutional neural network
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Human Emotion Recognition With Relational Region-Level Analysis
Li, W., Dong, X., & Wang, Y. (2023) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3064918 · Cited by 43
Elucidating image-to-set prediction: An analysis of models, losses and datasets
Pineda, L., Salvador, A., Drozdzal, M., & Romero, A. (2019) · arXiv · doi:10.48550/arxiv.1904.05709 · Cited by 6
Metadata
- Authors
- Wang, Z., Chen, T., Li, G., Xu, R., & Lin, L.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 464-472
- OpenAlex
- W2963300078
- Cited by
- 326
- References
- 33
- FWCI
- 8.33
- Type
- conference paper
- Language
- en
- Open access
- green
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