recognition well and efficient, with low energy consumption.

Inspired by the aforementioned findings, we design our TKD framework that utilizes the knowledge distillation techniques. It transfers temporal knowledge from the heavy model to a light model to boost visual processing efficiency while maintaining the heavy model’s (a.k.a., oracle model) performance. Figure \[set-subset\] illustrates the overall goal of this work. In this figure, we show how TKD improves recognition accuracy over different scenes, compared to the oracle model which we assume to be a perfect model. Also, we show the baseline model which is a tiny model with low accuracy compared to oracle recognition due to a much lower number of parameters. TKD achieves higher accuracy by adapting itself to the observed environment. In the case of an indoor scene, the TKD recognition accuracy improves significantly over objects which are more probable to be observed inside a building. In the outdoor case, TKD recognition accuracy improves over the objects such as a car, bus, and truck which are more probable to be observed outside. For a similar amount of model parameters as the baseline tiny model, the TKD will achieve much better performance over the more probable objects by dynamically learning 