POCA combines Pareto optimization with curriculum alignment to improve multi-reward reinforcement learning for visual text generation without relying on weighted sums.
A compar- ative survey of deep active learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
MOSAIC is a scaling-aware data selection framework that outperforms baselines in training end-to-end autonomous driving planners, achieving comparable or better EPDMS scores with up to 80% less data.
Localization uncertainty visualization in AI predictions improves human annotation quality and speed by redirecting effort toward high-uncertainty boxes.
citing papers explorer
-
POCA: Pareto-Optimal Curriculum Alignment for Visual Text Generation
POCA combines Pareto optimization with curriculum alignment to improve multi-reward reinforcement learning for visual text generation without relying on weighted sums.
-
Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems
MOSAIC is a scaling-aware data selection framework that outperforms baselines in training end-to-end autonomous driving planners, achieving comparable or better EPDMS scores with up to 80% less data.
-
From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review
Localization uncertainty visualization in AI predictions improves human annotation quality and speed by redirecting effort toward high-uncertainty boxes.