Mohammad Farhadi\
    Arizona State University\
    [mfarhadi@asu.edu]{}
- |
    Yezhou Yang\
    Arizona State University\
    [yz.yang@asu.edu]{}
bibliography:
- 'egbib.bib'
title: 'TKD: Temporal Knowledge Distillation for Active Perception'
---

Introduction {#sec:intro}
============

Object detection plays a critical role in a variety of mobile robot tasks such as obstacle avoidance [@carrio2018drone; @yaghoubi2019worst], detection and tracking [@breuers2018detection] and object searching [@ye2018active; @ye2019gaple]. During the last decade, we have witnessed the great success of Convolutional Neural Networks (CNNs)-based methods in the object detection task. This success has led researchers to explore deeper models such as RetinaNet [@lin2018focal] or Faster-RCNN [@ren2015faster], which yield high recognition accuracy. The “secret” sauce behind the success of these deeper and deeper CNNs models is the stacking of repetitive layers and increasing the number of model parameters [@chen2017learning]. This practice becomes possible while the applications are running on infrastructures with high processing capabilities.

![An illustration of our TKD model’s actual performance: F-1 score distribution over example object categories in different environments using TKD.[]{data-label="set-subset"}](./images/jamal.png){width="7.5cm"}

However, the disadvantages of this practice are obvious and the high performance is achieved by the significant growth of the model complexity: stacking up layers and increasing the model parameters which are computationally expensive and also increase the inference 