SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
Zhejiang University · Columbia University · +3 more institutions
Abstract
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…
Citation impact
- FWCI
- 70.23
- Percentile
- 100%
- References
- 68
Authors
7Topics & keywords
- Closed captioning
- Computer science
- Convolutional neural network
- Artificial intelligence
- Feature (linguistics)
- Context (archaeology)
- Encoding (memory)
- Sentence
- Quality Education