Data hungry – Un/Semi/Weakly-supervised learning
This biases vision researchers to work on tasks where annotation is easy instead of tasks that are important
Generalization issue – Sensor/Environment changes
Deep Nets perform well on benchmark datasets, but can fail badly on real-world images outside the dataset
Efficient learning – Multi-task Learning, Dynamic Neural Net
Real-world applications require efficient neural networks for multiple tasks to run simultaneously
