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GaitGCI: Generative Counterfactual Intervention for Gait Recognition

CVPR 2023 · Probabilistic and scientific modeling › Causal discovery · approach: Latent-variable causal estimation · 70 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add counterfactual intervention learning (CIL) (ablation)GaitGCI backbone · CASIA-BRank-1 mean (%): 1.6%Helped
Add generative factual attention (GFA) (ablation)CIL · CASIA-BRank-1 mean (%): 0.9%Helped
Add generative counterfactual attention (GCA) (ablation)CIL · CASIA-BRank-1 mean (%): 0.8%Helped
Use both GFA and GCA (ablation)CIL · CASIA-BRank-1 mean (%): 1.4%Helped
Replace static convolution with dynamic convolution (ablation)DCDC · CASIA-BRank-1 mean (%): 92.1 -> 92.6Helped
Add matrix decomposition to dynamic convolution (ablation)DCDC · CASIA-BRank-1 mean (%): 92.6 -> 92.7Helped
Add the rank-based diversity constraint to matrix-decomposed dynamic convolution (ablation)DCDC · CASIA-BRank-1 mean (%): 92.7 -> 93.1Helped
Increase GaitGCI channel capacity from C to 1.5C (ablation)GaitGCI · CASIA-BRank-1 mean (%): 93.1 -> 94.0Helped
Increase GaitGCI channel capacity from 1.5C to 2C (ablation)GaitGCI · CASIA-BRank-1 mean (%): 94.0 -> 94.5Helped
Use GaitGCI instead of 3DLocalGait recognition · OU-MVLPRank-1 mean (%): 90.9 -> 92.1Helped
Use GaitGCI instead of GaitGLSilhouette-based gait recognition · GREWRank-1 (%): 47.3 -> 68.5Helped
Use GaitGCI instead of SMPLGaitGait recognition · Gait3D, 128×88Rank-1 (%): 53.2 -> 57.2Helped

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