מושגי ליבה
The author argues that by optimizing CAMs in an end-to-end manner, the reliance on refinement processes can be reduced, leading to more reliable and accurate CAMs for weakly supervised semantic segmentation. The proposed method, Co-training with Swapping Assignments (CoSA), leverages a dual-stream framework to achieve exceptional performance.
תקציר
The study introduces CoSA, an end-to-end weakly supervised semantic segmentation model that optimizes CAMs online without the need for offline refinement. By incorporating guided CAMs, soft perplexity-based regularization, dynamic threshold searching, and contrastive separation techniques, CoSA outperforms existing methods on VOC and COCO datasets. The approach addresses issues of inconsistent and inaccurate class activation maps while achieving superior results in challenging segmentation tasks.
Key points:
Class activation maps (CAMs) are commonly used in weakly supervised semantic segmentation.
Existing studies often resort to offline CAM refinement, limiting generalizability.
CoSA aims to reduce CAM inconsistency by optimizing them online.
The method incorporates guided CAMs and three techniques: soft perplexity-based regularization, dynamic threshold searching, and contrastive separation.
CoSA achieves exceptional performance on VOC and COCO datasets.
סטטיסטיקה
CoSA demonstrates mIoU of 76.2% on VOC validation dataset.
CoSA achieves mIoU of 51.0% on COCO validation dataset.
ציטוטים
"Our method optimizes the CAMs and segmentation prediction simultaneously thanks to the differentiability of CAMs."
"CoSA greatly surpasses existing WSSS methods."