Mamba attention gate workflow.

<p>Encoder () and decoder () features are weighted, combined, and passed through ReLU and sigmoid activations to compute attention weights (<i>ψ</i>). These weights refine , producing the final feature map () for improved segmentation.</p>

Saved in:

Bibliographic Details
Main Author:	Asim Niaz (17655568) (author)
Other Authors:	Muhammad Umraiz (10176287) (author), Shafiullah Soomro (4804227) (author), Kwang Nam Choi (4804230) (author)
Published:	2025
Subjects:	Cell Biology Genetics Developmental Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified printed circuit boards mean average precision global feature extraction enhance feature representation xlink "> defects inspired attention mechanism combines vision transformers imbalanced defect distributions precise defect segmentation vision transformer pcb defects attention fusion accurate segmentation limited generalization improve robustness hybrid framework deep learning consumer electronics based techniques
Tags:	Add Tag No Tags, Be the first to tag this record!

Mamba attention gate workflow.

Similar Items