DeCoDrift stabilizes decoder coupling in closed-loop foundation segmentation by constraining prompt updates without retraining or ground truth.
Robustness of sam: Segment anything under corruptions and beyond
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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GleSAM integrates latent diffusion into SAM and SAM2 to boost segmentation robustness on low-quality images using minimal extra parameters and a new LQSeg dataset.
PGE-SAM adds a Prompt Guidance Generator, multi-scale feature interaction, and foreground reconstruction loss to SAM for better interactive segmentation on degraded images, plus a new DM-Seg benchmark.
GleSAM++ improves SAM robustness on degraded images by using generative enhancement, feature alignment, and adaptive degradation prediction while adding few parameters.
MobileSAM is a 60x smaller distilled version of SAM that matches original performance and runs 5x faster than concurrent FastSAM while supporting CPU inference.
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.
citing papers explorer
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DeCoDrift: Stabilizing Decoder Coupling in Closed-Loop Foundation Segmentation
DeCoDrift stabilizes decoder coupling in closed-loop foundation segmentation by constraining prompt updates without retraining or ground truth.
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Segment Any-Quality Images with Generative Latent Space Enhancement
GleSAM integrates latent diffusion into SAM and SAM2 to boost segmentation robustness on low-quality images using minimal extra parameters and a new LQSeg dataset.
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PGE-SAM: Prompt-Guided Feature Enhancement for Interactive Segmentation under Degradation
PGE-SAM adds a Prompt Guidance Generator, multi-scale feature interaction, and foreground reconstruction loss to SAM for better interactive segmentation on degraded images, plus a new DM-Seg benchmark.
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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.
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Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
MobileSAM is a 60x smaller distilled version of SAM that matches original performance and runs 5x faster than concurrent FastSAM while supporting CPU inference.
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A Survey on Foundation Models for Personalized Federated Intelligence
The survey introduces personalized federated intelligence (PFI) as a framework integrating federated learning and foundation models to support privacy-aware personalization of AI models.