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E-VAT: An Asymmetric End-to-End Approach to Visual Active Exploration and Tracking.
Alberto Dionigi
Alessandro Devo
Leonardo Guiducci
Gabriele Costante
Published in:
IEEE Robotics Autom. Lett. (2022)
Keyphrases
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end to end
active exploration
congestion control
admission control
real time
problem based learning
active learning
scalable video
feature selection
case study
reinforcement learning
learning environment
small sample
application layer