Learning the Target Priors Before Image Translation: A Decoupled Training Paradigm for Cross-Modal Image Translation in Remote Sensing
Cross-modal image translation in remote sensing must preserve source-observed content while matching the target-domain distribution. Existing methods jointly learn the target prior and cross-modal dependence from scarce paired data, overlooking a key asymmetry: only the latter intrinsically requires cross-modal correspondence. We formalize this distinction through conditional-score and denoising-risk analyses and propose Learning the Target Priors Before Image Translation (LTP-BIT), a prior-first paradigm that decouples the two learning tasks. LTP-BIT first learns a target-domain generative pr
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- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “Learning the Target Priors Before Image Translation: A Decou” ≈ “Tongyi-MAI/Z-Image-Turbo””
- FuzzySimilar title/name (fuzzy) · 84%amitness/learning →
“Fuzzy title match (0.92): “Learning the Target Priors Before Image Translation: A Decou” ≈ “amitness/learning””
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
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- LinkedLinked via arxiv author · 85%Keyan Hu →
“Learning the Target Priors Before Image Translation: A Decoupled Training Paradigm for Cross-Modal Image Translation in ”
- LinkedLinked via arxiv author · 85%Mingtao Wang →
“Learning the Target Priors Before Image Translation: A Decoupled Training Paradigm for Cross-Modal Image Translation in ”
- LinkedLinked via arxiv author · 85%Ziyu Zhou →
“Learning the Target Priors Before Image Translation: A Decoupled Training Paradigm for Cross-Modal Image Translation in ”