function.

Ablation Study {#sec:ablation}
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As shown in table \[TKD\_hollywood\], we compare different strategies to highlight the effectiveness of our proposed novel loss and key frame selector. We consider the output of the oracle model as ground truth and evaluating different methods over it. Here, we compare five methods: 1) TKD with random key frame selection; 2) TKD with Scene Change detection; 3) Tiny-Yolo without any training; 4) Combination of Tiny-Yolo and Yolo-v3 without training; 5) TKD with our proposed key frame selection method.

In the following experiments, we have set the $\lambda$ to be $0.4$ which is obtained heuristically. In \[sec:discussion\], we will go through the findings which we observed in our search for the best $\lambda$.

**Random Selection:** Here, instead of selecting key frames by our proposed method, decision modules selects frames purely randomly for further processing. During the testing phase, the probability is set to be $27\%$ (to make sure it selects more frames than our method ($25\%$ on average)). Random selection achieves $0.75$ $F_1$ score (IOU=$0.5$) in the Hollywood scene dataset and achieves $0.65$ $F_1$ score (IOU=0.5) in the pursuit of happiness. On average, it reaches a frame-rate of $89$ frames per second (FPS).

**Scene Change 