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BackTrack: Robust template update via Backward Tracking of candidate template
An effective method to tackle these challenges is template update, which updates the template to reflect the change of appearance in the target object during tracking.
Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss
We demonstrate the effectiveness of our trainable quantizer on ImageNet dataset with various network architectures such as ResNet-18, -34 and AlexNet, on which it outperforms existing methods to achieve the state-of-the-art accuracy.
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