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Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry and pose the challenges of not having adequate computing resources and of high costs involved in human labeling efforts.
An analysis of approximations for maximizing submodular set functions—i
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Rich feature hierarchies for accurate object detection and semantic segmentation
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
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Yolo9000: Better, faster, stronger
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Submodular Optimization and Data Processing
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