The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
Open accessAbstract
Visual attention has been successfully applied in structural prediction tasks such as visual captioning and question answering. Existing visual attention models are generally spatial, i.e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image. However, we argue that such spatial attention does not necessarily conform to the attention mechanism - a dynamic feature extractor that combines contextual fixations over time, as CNN features are naturally spatial, channel-wise and multi-layer. In this paper, we introduce a novel convolutional neural network dubbed SCA-CNN that incorporates Spatial and Channel-wise Attentions in a CNN. In the task of image captioning, SCA-CNN dynamically modulates the sentence generation context in multi-layer feature maps, encoding where (i.e., attentive spatial locations at multiple layers) and what (i.e., attentive channels) the visual attention is. We evaluate the proposed SCA-CNN architecture on three benchmark image captioning datasets: Flickr8K, Flickr30K, and MSCOCO. It is consistently observed that SCA-CNN significantly outperforms state-of-the-art visual attention-based image captioning methods.
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Cites
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Cited by
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“SCACNN [17] CVPR2017 ICap a)compute channel attention map and spatial attention map using visual features and previous hidden state b) stack channel attention and spatial attention in serial a) focus the network on discriminative regions b) emphasize important channels c) capture global information d) avoid high compu…”
BackgroundMethodInfluential
Metadata
- Authors
- Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T.-S.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 6298-6306
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2550553598
- Cited by
- 1,992
- References
- 14
- FWCI
- 55.41
- Type
- conference paper
- Language
- en
- Open access
- green
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