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Research map / Architectures and efficiency / Neural architecture search

Combined Depth Space Based Architecture Search for Person Re-Identification

CVPR 2021 · Architectures and efficiency › Neural architecture search · approach: Latency-aware architecture search · 169 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add adaptive depth through CDBlocksCDNet versus CNet · Market1501mAP: 83.5 -> 83.7Helped
Use top-2 rather than top-1 branch sampling during searchCDNet · Market1501 search evaluationmAP: 83.2 -> 83.7Helped
Increase the number of sampled branches beyond top-2CDNet search · Market1501 search evaluationmAP: 83.7 -> 83.5 -> 83.0Hurt
Add FNeck local features using 2 partitions (ablation)CDNet · Market1501mAP: 81.5 -> 83.7Helped
Increase FNeck partitions from 2 to 3 (ablation)CDNet · Market1501mAP: 83.7 -> 82.7Hurt
Use 4 rather than 2 FNeck partitions (ablation)CDNet · Market1501rank-1: 93.7 -> 94.2Helped
Replace BNNeck with BLNeck (ablation)CDNet body · Market1501mAP: 77.6 -> 81.1Helped
Add FNeck to BLNeck to form FBLNeck (ablation)CDNet body · Market1501mAP: 81.1 -> 83.7Helped
Replace the two-branch CBlock with a single branch (ablation)CDNet · Market1501mAP: 83.7 -> 79.7Hurt
Fix CBlock kernel combinations to (3,5) (ablation)CDNet · Market1501mAP: 83.7 -> 82.0Hurt
Attach FBLNeck (ablation)OSNet · Market1501mAP: 82.2 -> 83.4Helped
Reduce image resolution while keeping width multiplier at 1.0 (ablation)CDNet · Market1501mAP: 86.0 -> 82.3Hurt

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