GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.
arXiv preprint arXiv:2304.06022 (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
GLASSNet outperforms prior methods on salient object detection benchmarks by freezing SAMv2, adding a spatially aware adapter, and fusing outputs from global and local decoders.
citing papers explorer
-
Towards Any-Quality Image Segmentation via Generative and Adaptive Latent Space Enhancement
GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.
-
Global-Local Feature Decoding with Adapter-Guided SAMv2 for Salient Object Detection
GLASSNet outperforms prior methods on salient object detection benchmarks by freezing SAMv2, adding a spatially aware adapter, and fusing outputs from global and local decoders.