FlowDIS uses flow matching to transport image distributions to mask distributions, optionally conditioned on text, and outperforms prior DIS methods by 5.5% on F_beta^omega and 43% on MAE.
Aligning genera- tive denoising with discriminative objectives unleashes diffu- sion for visual perception.arXiv preprint arXiv:2504.11457
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
years
2026 3representative citing papers
OTCA improves GRPO training for visual generation by estimating step importance in trajectories and adaptively weighting multiple reward objectives.
citing papers explorer
-
FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matching
FlowDIS uses flow matching to transport image distributions to mask distributions, optionally conditioned on text, and outperforms prior DIS methods by 5.5% on F_beta^omega and 43% on MAE.
-
Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation
OTCA improves GRPO training for visual generation by estimating step importance in trajectories and adaptively weighting multiple reward objectives.
- EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design