from the oracle model.

To summarize our contributions: 1) we propose an end-to-end trainable framework to transfer the temporal knowledge (a.k.a., the perception of the moment) of the oracle model to the student model; 2) we propose a novel teacher-bonded loss for knowledge distillation which has a simple structure and performs inferences briskly; and 3) we propose an efficient method to select key frames from the dynamic scene, that indicate the right timing to train student model and to improve the detection accuracy. We design and conduct empirical experiments on both the public datasets (the Youtube Object dataset and the Hollywood Scene dataset) as well as on two long videos with multiple scene changes, which validate each of the aforementioned novel design choices, by observing a fast object recognition performance while maintaining high detection accuracy.

Related Work
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Visual recognition systems, ranging from object recognition [@lin2018focal], action recognition [@lea2016learning], to scene recognition [@zhou2014learning] have gained attention in recent years. Significant improvements in recognition accuracy have resulted in economic and societal benefits in AI applications such as autonomous vehicles [@khayatian2019crossroads+; @khayatian2018rim], and IoT systems [@tonekaboni2018edge; @tonekaboni2018scouts].

**Object Detection:** Object detection methods based on Convolutional Neural Networks (CNNs) have shown promising results 