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This paper proposes a novel local matting algorithm based on sample-pair propagation and iterative refinement. Since sample-pairs of the foreground and background in the neighborhood are limited, they fail to fit the linear model well. We propose a sample-pair propagation scheme which propagates the confident sample-pair of each pixel to its neighbors so(More)
For objects with large appearance variations, it has been proved that their detection performance can be effectively improved by clustering positive training instances into subcategories and learning multi-component models for the subcategories. However, it is not trivial to generate subcategories of high quality, due to the difficulty in measuring the(More)
In object detection, the offline trained detector's performance may be degraded in a particular deployed environment, because of the large variation of different environments. In this work, we propose a data level object detector adaptation method to new environments. By recording a small amount of offline data, it's fully compatible with offline training(More)
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