Attention-guided Feature Distillation for Semantic Segmentation
In contrast to existing complex methodologies commonly employed for distilling knowledge from a teacher to a student, the pro-posed method showcases the efficacy of a simple yet powerful method for utilizing refined feature maps to transfer attention. The proposed method has proven to be effective in distilling rich information, outperforming existing methods in semantic segmentation as a dense prediction task. The proposed Attention-guided Feature Distillation (AttnFD) method, em-ploys the Convolutional Block Attention Module (CBAM), which refines feature maps by taking into account both channel-specific and spatial information content. By only using the Mean Squared Error (MSE) loss function between the refined feature maps of the teacher and the student,AttnFD demonstrates outstanding performance in semantic segmentation, achieving state-of-the-art results in terms of mean Intersection over Union (mIoU) on the PascalVoc 2012 and Cityscapes datasets. The Code is available at https://github.com/AmirMansurian/AttnFD.
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Cityscapes
PASCAL VOC
