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FlowCIR slashes training resource requirements by 90% while boosting robustness against negation in zero-shot image retrieval tasks.
Decoupling radial and angular dynamics in vision-language model adaptation unlocks significant gains in few-shot performance, outperforming existing flow matching methods.
Achieve high-fidelity, training-free visual generation from coarse inputs by cleverly steering pretrained diffusion models with a noise-adaptive h-transform.
