In-depth analysis of CGM technology
The innovative nature of the Confidence Guided Matting pipeline is reflected in the two-stage treatment:
- confidence projection stage: The base network outputs a per-pixelprospect probability(0-1) andconfidence score(0-100%)
- Adaptive refinement stage: A specially designed U-Net structure is enabled for sub-pixel level corrections for pixel regions with confidence <90% (usually present at hairline/glass edges), which contains:
- Expanded convolutional layer (capturing multi-scale features)
- Attention Mechanism Module (Enhanced Edge Response)
- Residual connection (maintains original color information)
The technical white paper shows that CGM makesJaccard's indexIt improves 9.21 TP3T on the DIS5k test set, especially in the animal hair (14.71 TP3T improvement) and transparent object (18.31 TP3T improvement) scenes.
This answer comes from the articleBEN2: Deep learning model for fast background removal from images, videosThe
Related files download url
You need to log in to download this resource. Go to log in
© Download resources copyright belongs to the author; all resources on this site are from the network, for learning purposes only, please support the original version!
May not be reproduced without permission:AI productivity tools " How specifically does BEN2's CGM technology pipeline improve edge segmentation accuracy?
