87 research outputs found
PKCAM: Previous Knowledge Channel Attention Module
Recently, attention mechanisms have been explored with ConvNets, both across
the spatial and channel dimensions. However, from our knowledge, all the
existing methods devote the attention modules to capture local interactions
from a uni-scale. In this paper, we propose a Previous Knowledge Channel
Attention Module(PKCAM), that captures channel-wise relations across different
layers to model the global context. Our proposed module PKCAM is easily
integrated into any feed-forward CNN architectures and trained in an end-to-end
fashion with a negligible footprint due to its lightweight property. We
validate our novel architecture through extensive experiments on image
classification and object detection tasks with different backbones. Our
experiments show consistent improvements in performances against their
counterparts. Our code is published at https://github.com/eslambakr/EMCA
Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading
Diabetes is one of the most common disease in individuals. \textit{Diabetic
retinopathy} (DR) is a complication of diabetes, which could lead to blindness.
Automatic DR grading based on retinal images provides a great diagnostic and
prognostic value for treatment planning. However, the subtle differences among
severity levels make it difficult to capture important features using
conventional methods. To alleviate the problems, a new deep learning
architecture for robust DR grading is proposed, referred to as SEA-Net, in
which, spatial attention and channel attention are alternatively carried out
and boosted with each other, improving the classification performance. In
addition, a hybrid loss function is proposed to further maximize the
inter-class distance and reduce the intra-class variability. Experimental
results have shown the effectiveness of the proposed architecture.Comment: Accepted to ICIP 202
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