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Imitating the behaviors of an arbitrary visual tracking algorithm enables many higher level tasks such as tracker identification and efficient tracker-fusion. It is also useful for discovering the features essential in a black-box tracker or learning from several trackers to form a super-tracker. In this study, we propose a non-linear feature fusion framework, “MIMIC” that imitates many popular trackers...
The present paper aims to propose a simple method to realize mutual information maximization for better interpretation and generalization. To train neural networks and obtain better performance, neurons should impartially consider as many input patterns as possible. Simultaneously, and especially for ease of interpretation, they should represent characteristics specific to certain input patterns as...
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