Virtual KITTI 2 supplies synthetic clones of real KITTI driving sequences with added weather and camera variants and multi-modal ground-truth annotations for autonomous driving vision research.
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Compares feedforward, recurrent, sequence-to-sequence and temporal convolutional neural networks for short-term electric load forecasting through experiments on two real datasets.
Faster RCNN is extended with a track branch and trained end-to-end on concatenated video frames to unify detection and re-identification, reaching 57.79% mAP on the AIC19 vehicle dataset.
citing papers explorer
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Virtual KITTI 2
Virtual KITTI 2 supplies synthetic clones of real KITTI driving sequences with added weather and camera variants and multi-modal ground-truth annotations for autonomous driving vision research.
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Deep Learning for Time Series Forecasting: The Electric Load Case
Compares feedforward, recurrent, sequence-to-sequence and temporal convolutional neural networks for short-term electric load forecasting through experiments on two real datasets.
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A unified neural network for object detection, multiple object tracking and vehicle re-identification
Faster RCNN is extended with a track branch and trained end-to-end on concatenated video frames to unify detection and re-identification, reaching 57.79% mAP on the AIC19 vehicle dataset.