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Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition
We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types.
Understanding data augmentation for classification: when to warp?
In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier.
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