SceneMiner shows that identity-preserving multi-task fine-tuning removes cross-task interference by zero-initializing new heads and freezing shared-stream parameters, enabling unified BEV scene mining with preserved original heads.
Zhang, Alexander Sax, Amir Zamir, Leonidas Guibas, and Jitendra Ma- lik
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
SceneMiner: Identity-Preserving Multi-Task Fine-Tuning for Unified BEV Scene Mining
SceneMiner shows that identity-preserving multi-task fine-tuning removes cross-task interference by zero-initializing new heads and freezing shared-stream parameters, enabling unified BEV scene mining with preserved original heads.