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Found 2 papers, 2 papers with code
PNAS-MOT: Multi-Modal Object Tracking with Pareto Neural Architecture Search
Multiple object tracking is a critical task in autonomous driving.
G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection
To address this issue, we propose the Generalizable loss (G-loss), which is an OoD-aware objective, preventing NAS from over-fitting by using gradient descent to optimize parameters not only on a subset of easy-to-learn features but also the remaining predictive features for generalization, and the overall framework is named G-NAS.
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