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Deeper and Wider Siamese Networks for Real-Time Visual Tracking

CVPR 2019 · Architectures and efficiency › CNN design · approach: Efficient CNN scaling and dynamic inference · 1,109 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace AlexNet with CIResNet-22SiamFC · OTB-2015AUC: 0.58 -> 0.64Helped
Replace AlexNet with CIResNet-22SiamFC · VOT-17EAO: 0.19 -> 0.23Helped
Replace AlexNet with CIResNet-22SiamRPN · OTB-2015AUC: 0.64 -> 0.67Helped
Replace AlexNet with CIResNet-22SiamRPN · VOT-17EAO: 0.24 -> 0.30Helped
Add cropping-inside residual units to remove padding-affected features (ablation)CIResNet-22 SiamFC · VOT-16EAO: 0.213 -> 0.301Helped
Add cropping-inside residual units to remove padding-affected features (ablation)CIResNet-20 SiamFC · VOT-16EAO: 0.204 -> 0.271Helped
Add cropping-inside residual units to remove padding-affected features (ablation)CIResInception-22 SiamFC · VOT-16EAO: 0.227 -> 0.282Helped
Use the proposed CIR-D downsampling unit instead of original downsampling (ablation)CIResNet-22 SiamFC · VOT-16EAO: 0.292 -> 0.301Helped
Use CIR-D instead of cropping after addition in the downsampling block (ablation)CIResNet-22 SiamFC · VOT-16EAO: 0.287 -> 0.301Helped
Increase receptive field beyond the best-sized setting (ablation)CIResNet-22 SiamFC · VOT-16EAO: 0.30 -> 0.25Hurt
Reduce output feature size to 2 (ablation)CIResNet-22 SiamFC · VOT-16EAO: 0.30 -> 0.25Hurt
Increase stride from 8 to 16 (ablation)CIResNet-22 SiamFC · VOT-16EAO: 0.30 -> 0.23Hurt

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