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Human Trajectory Prediction With Momentary Observation

CVPR 2022 · Label-efficient and robust learning › Recognition and long-tail · approach: Dense features and foundation priors · 36 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add full MOE with soft pre-training to PCCSNetPCCSNet · ETH/UCY (average)ADE: 0.24 -> 0.20Helped
Add full MOE with soft pre-training to PCCSNetPCCSNet · SDDADE: 9.19 -> 8.40Helped
Add full MOE with soft pre-training to NextNext · ETH/UCY (average)ADE: 0.49 -> 0.45Helped
Add full MOE with soft pre-training to Trajectron++Trajectron++ · ETH/UCY (average)ADE: 0.36 -> 0.31Helped
Replace the original feature extractor with MOE, without pre-training (ablation)PCCSNet · ETH/UCY (average)ADE: 0.24 -> 0.22Helped
Replace the original feature extractor with MOE, without pre-training (ablation)PCCSNet · SDDADE: 9.19 -> 8.75Helped
Add context restoration pre-training to MOE (ablation)PCCSNet · SDDADE: 8.75 -> 8.63Helped
Add masked trajectory complement pre-training to MOE (ablation)PCCSNet · SDDADE: 8.75 -> 8.51Helped
Use both context restoration and masked trajectory complement pre-training (ablation)PCCSNet + MOE · ETH/UCY (average)ADE: 0.22 -> 0.20Helped
Use both context restoration and masked trajectory complement pre-training (ablation)PCCSNet + MOE · SDDADE: 8.75 -> 8.40Helped
Vary masked trajectory complement mask size from 4 to 8 (ablation)PCCSNet + MOE · ETH/UCY (average)ADE: 0.20; 0.20; 0.20No clear effect
Increase masked trajectory complement mask size from 8 to 12 (ablation)PCCSNet + MOE · SDDADE: 8.47 -> 8.63Hurt

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