PASTA uses ViT feature distribution analysis and SAM to achieve up to 88.3% target and 63.5% anomaly IoU with 75.8% reduced training time under weak image-level supervision on custom datasets.
Zero-shot semantic segmentation for robots in agriculture,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
PASTA: Vision Transformer Patch Aggregation for Weakly Supervised Target and Anomaly Segmentation
PASTA uses ViT feature distribution analysis and SAM to achieve up to 88.3% target and 63.5% anomaly IoU with 75.8% reduced training time under weak image-level supervision on custom datasets.