U2Diffine augments diffusion denoising with negative log-likelihood loss and first-order uncertainty propagation to jointly perform trajectory completion and provide per-state heteroscedastic uncertainty for multi-agent paths.
Denoising diffusion implicit models
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EditTransfer++ delivers state-of-the-art faithfulness to visual editing examples and faster inference by removing text conditioning during fine-tuning and applying best-worst contrastive refinement plus condition compression.
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Heteroscedastic Diffusion for Multi-Agent Trajectory Modeling
U2Diffine augments diffusion denoising with negative log-likelihood loss and first-order uncertainty propagation to jointly perform trajectory completion and provide per-state heteroscedastic uncertainty for multi-agent paths.
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EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing
EditTransfer++ delivers state-of-the-art faithfulness to visual editing examples and faster inference by removing text conditioning during fine-tuning and applying best-worst contrastive refinement plus condition compression.
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