OpenLongTail: Generative Scaling of Long-Tail Driving Data
Scaling robust driving policies is fundamentally bottlenecked by the scarcity of edge cases in curated datasets. While the real world continuously captures these critical events, such long-tail events remain underutilized when collected from heterogeneous sources. Specifically, diverse but valuable in-the-wild long-tail videos lack the full view coverage required for training policy models, often missing multi-view poses or originating solely from monocular dash cameras. This modality gap prevents these ubiquitous observations from being converted into scalable training data for long-tail gene
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Lulin Liu →
“OpenLongTail: Generative Scaling of Long-Tail Driving Data”
- LinkedLinked via arxiv author · 85%Nuo Chen →
“OpenLongTail: Generative Scaling of Long-Tail Driving Data”
- LinkedLinked via arxiv author · 85%Linyan Wang →
“OpenLongTail: Generative Scaling of Long-Tail Driving Data”
- LinkedLinked via arxiv author · 85%Bangya Liu →
“OpenLongTail: Generative Scaling of Long-Tail Driving Data”
- LinkedLinked via arxiv author · 85%Wenyan Cong →
“OpenLongTail: Generative Scaling of Long-Tail Driving Data”
- LinkedLinked via arxiv author · 85%Hezhen Hu →
“OpenLongTail: Generative Scaling of Long-Tail Driving Data”
- LinkedLinked via arxiv author · 85%Boris Ivanovic →
“OpenLongTail: Generative Scaling of Long-Tail Driving Data”
- LinkedLinked via arxiv author · 85%Jiahao Wang →
“OpenLongTail: Generative Scaling of Long-Tail Driving Data”