time significantly. Hence, these models are not suitable for real-time and embedded visual processing systems, and thus impede their deployment in the era of intelligent robots and autonomous vehicles. The same concerns also lie in the energy conservation and computation limits, since deep models require a large number of matrix multiplications, which are time-consuming and energy-demanding for mobile applications.

The aforementioned concerns trigger various approaches, such as using the alignment of memory and SIMD (Single Instruction, Multiple Data) operations to boost matrix operations [@gong2014compressing]. More recently, studies [@chen2017learning] and [@hinton2015distilling] proposed transferring the knowledge of deep models to shallow models while maintaining the recognition accuracy. Although these approaches do improve the model efficiency, they ignore the temporal dependencies among the frames from dynamic scenes, which is one of the critical capabilities to maintain high recognition accuracy while being energy-aware. The motivation for our TKD model comes from the visual adaptation phenomenon observed in the Human Visual Systems (HVS). Visual adaption involves temporary changes in the human perception system when exposed to intense or new stimulus and by the lingering aftereffects when the stimulus is removed [@webster2015visual]. Other studies from [@webster2015visual] show that the visual system adapts to the changes in 