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Found 4 papers, 1 papers with code
BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture
The proposed loss functions in BEA improve the confidence score calibration and lower the uncertainty error, which results in a better distinction of true and false positives and, eventually, higher accuracy of the object detection models.
VALERIE22 -- A photorealistic, richly metadata annotated dataset of urban environments
The VALERIE tool pipeline is a synthetic data generator developed with the goal to contribute to the understanding of domain-specific factors that influence perception performance of DNNs (deep neural networks).
Increasing pedestrian detection performance through weighting of detection impairing factors
The resulting detector is then used to calibrate an empirical weighting loss, which weights samples of real training data and their corresponding detection impairing factors.
Ranked #1 on
Pedestrian Detection
on CityPersons
(using extra training data)
Towards Machine Learning-Based Optimal HAS
We first use a modified existing optimization formulation to calculate optimal adaptation paths with a minimum number of quality switches for a wide range of videos and for challenging mobile throughput patterns.
