SceneSelect discovers latent scene categories via clustering, trains a classifier to assign inputs, and dispatches to expert trajectory predictors, reporting 10.5% average gains over single-model and ensemble baselines on ETH-UCY, SDD, and NBA.
Expert Systems with Applications 301, 130474 (2026)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
SceneSelect: Selective Learning for Trajectory Scene Classification and Expert Scheduling
SceneSelect discovers latent scene categories via clustering, trains a classifier to assign inputs, and dispatches to expert trajectory predictors, reporting 10.5% average gains over single-model and ensemble baselines on ETH-UCY, SDD, and NBA.