FlowCIR frames ZS-CIR as conditional flow matching transport on fixed VLM embeddings plus an inference-time Multi-Negative Steering fix for negation, reporting competitive benchmark results at far lower training cost.
Robust classification via a single diffusion model
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
DiffOR reformulates ordinal regression as continuous generative modeling using diffusion models with dual-decoupling to capture soft semantic transitions.
MEFA enables exact full-gradient white-box attacks on iterative stochastic purification defenses like diffusion and Langevin EBMs by trading recomputation for lower memory, revealing vulnerabilities missed by approximate-gradient methods.
citing papers explorer
-
FlowCIR: Semantic Transport via Flow Matching for Zero-Shot Composed Image Retrieval
FlowCIR frames ZS-CIR as conditional flow matching transport on fixed VLM embeddings plus an inference-time Multi-Negative Steering fix for negation, reporting competitive benchmark results at far lower training cost.
-
DiffoR: A Unified Continuous Generative Framework for Universal Ordinal Regression
DiffOR reformulates ordinal regression as continuous generative modeling using diffusion models with dual-decoupling to capture soft semantic transitions.
-
Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations
MEFA enables exact full-gradient white-box attacks on iterative stochastic purification defenses like diffusion and Langevin EBMs by trading recomputation for lower memory, revealing vulnerabilities missed by approximate-gradient methods.