Unified Video Dense Prediction from Disjoint Data
Scene understanding requires simultaneous prediction about geometry, appearance, and semantics. However, existing task-specific annotations are fragmented across incompatible, domain-specific datasets. Current unified systems circumvent this by restricting training to fully co-annotated data, or by incurring the large computational cost of pseudo-labeling. To mitigate this, we introduce UniD, a unified video model that jointly predicts eight dense scene properties-depth, surface normals, semantic segmentation, boundaries, human parts, albedo, shading, and materials-all learned from disjoint, d
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Yihong Sun →
“Unified Video Dense Prediction from Disjoint Data”
- LinkedLinked via arxiv author · 85%Seoung Wug Oh →
“Unified Video Dense Prediction from Disjoint Data”
- LinkedLinked via arxiv author · 85%Jiahui Huang →
“Unified Video Dense Prediction from Disjoint Data”
- LinkedLinked via arxiv author · 85%Bharath Hariharan →
“Unified Video Dense Prediction from Disjoint Data”
- LinkedLinked via arxiv author · 85%Joon-Young Lee →
“Unified Video Dense Prediction from Disjoint Data”