Figure 2 (IMAGE)
Caption
The overall framework for the attacker population optimization. (a) This paper utilizes the representation of the attacked ego system's trajectories to identify different attacker instances. Specifically, they apply an encoder-decoder architecture to learn the trajectory representation. The black solid arrows indicate the direction of data flow, and the red solid ones imply the direction of gradient flow. (b) This is a simple visualization case for one-time population updating. The locations of points imply the distances of representations, and the color shades indicate the attack ability, i.e., the attackers corresponding to deeper points are stronger attackers. For example, new attacker 3 is accepted as it is distant enough from other attackers, and the oldest attacker is removed; new attacker 2 is accepted, and the closest attacker 2 is removed as it is weaker.
Credit
Lei YUAN, Feng CHEN, Zongzhang ZHANG, Yang YU
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