Siamese Attention Networks with Adaptive Templates for Visual Tracking

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr class="thead"><td class="align_left">Template selecting strategy</td><td class="align_center">Accuracy↑</td><td class="align_center">Robustness↓</td><td class="align_center">EAO↑</td></tr><tr><td class="thead-hr" colspan="4"><hr/></td></tr><tr><td class="align_left">- (Baseline)</td><td class="align_center">0.600</td><td class="align_center">0.234</td><td class="align_center">0.414</td></tr><tr><td class="align_left">Random</td><td class="align_center">0.610</td><td class="align_center">0.216</td><td class="align_center">0.431</td></tr><tr><td class="align_left">Continuous</td><td class="align_center">0.612</td><td class="align_center">0.214</td><td class="align_center">0.436</td></tr><tr><td class="align_left">Our selecting strategy</td><td class="align_center">0.618</td><td class="align_center">0.202</td><td class="align_center">0.445</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Comparison results of different selecting methods on VOT2018. SiamRPN++ is baseline. “-” denotes without template selecting strategy.</div>

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Table 4

Table 4: Siamese Attention Networks with Adaptive Templates for Visual Tracking 