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.
In: ICLR (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
OT-NFM parameterizes the flow map directly with neural flows and uses optimal transport for consistent noise-data couplings to achieve ODE-free one-step generation while avoiding mean collapse.
citing papers explorer
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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.
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ODE-free Neural Flow Matching for One-Step Generative Modeling
OT-NFM parameterizes the flow map directly with neural flows and uses optimal transport for consistent noise-data couplings to achieve ODE-free one-step generation while avoiding mean collapse.