الفهرس 
EMCA: Efficient Multiscale Channel Attention Module for Deep Neural Networks
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Abstract
In this thesis, we tackle the following question: can one consolidate multi-scale aggregation while learning channel attention more efficiently? To this end, we avail channel-wise attention over multiple feature scales, which empirically shows its aptitude to replace the limited local and uni-scale attention modules. Atten- tion mechanisms have been explored with CNNs across the spatial and channel dimensions. However, all the existing methods devote attention to capturing local interactions from a uni-scale. Thus we propose EMCA, which is lightweight and can efficiently model the global context further; it is easily integrated into any feed-forward CNN architectures and trained in an end-to-end fashion. We validate our novel architecture through comprehensive experiments on image classification, object detection, and instance segmentation with different backbones.