sequence of erroneous decisions. In the end, If the LSTM makes a correct decision where the observed loss decrease $\Delta L \ < \ \sigma$ wherein our experiments $\sigma=-0.1$, the random factor $P_t$ will be reduced by $0.05$. If the LSTM model makes a wrong decision, we update the LSTM model and double the random factor $P_t$. Figure \[TKD\_loss\] shows an example output of key frames selected by our method. We apply knowledge distillation selectively to a few numbers of frames which partially addresses the aforementioned challenges 1) and 2) in Sec. \[distillation\].

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                                                                                                        **AP**    **F-1**     **AP**    **F-1**     **AP**    **F-1**
          Random Selection             0.71       0.75       0.54       0.68       0.48       0.49       0.65       0.65       0.55       0.58       0.35       0.43
       Scene Change Detection          0.68       0.58       0.47       0.50       0.23       0.35       0.54       0.58       0.45       0.53       0.35       0.44
    Tiny-Yolo [@redmon2018yolov3]      0.45       0.16       0.38       0.14       0.10       0.28       0.37       0.11       0.25       0.10       0.08       0.06
   Tiny-Yolo (73%) + Yolo-v3 (27%)     0.60       0.49       0.59       0.49       0.44       0.46       0.58       0.47       0.52       0.46       0.39       0.44
                 TKD                 **0.75**   **0.76**   **0.58**   **0.69**   **0.49**   **0.50**   **0.73**   **0.67**   **0.59**   **0.61**   **0.40**   **0.46**
  --------------------------------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ----------

Experiments {#sec:exp}
