RLFSeg repurposes pretrained generative models via Rectified Flow for direct latent-space image-to-mask mapping in text-based segmentation, outperforming diffusion-based methods especially in zero-shot cases.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Presents a new theoretically grounded hard-label attack with zero-query initialization and low-complexity optimization that outperforms prior methods across image datasets and models.
citing papers explorer
-
From Diffusion to Rectified Flow: Rethinking Text-Based Segmentation
RLFSeg repurposes pretrained generative models via Rectified Flow for direct latent-space image-to-mask mapping in text-based segmentation, outperforming diffusion-based methods especially in zero-shot cases.
-
Low-Cost Hard-Label Adversarial Attack with Theoretical Foundations
Presents a new theoretically grounded hard-label attack with zero-query initialization and low-complexity optimization that outperforms prior methods across image datasets and models.