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SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction

CVPR 2019 · Architectures and efficiency › RNNs and normalization · approach: Differentiable optimization layers · 558 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add SR-LSTM state refinement with two iterations, current-neighbor states and both selection modulesV-LSTM · ETH/UCY AVGMAD: 0.53 -> 0.45Helped
add motion gate to simple refinement (ablation)SR-LSTM · ETH/UCY AVGMAD: 0.52 -> 0.48Helped
add pedestrian-wise attention to simple refinement (ablation)SR-LSTM · ETH/UCY AVGMAD: 0.52 -> 0.49Helped
add both motion gate and pedestrian-wise attention (ablation)SR-LSTM simple refinement · ETH/UCY AVGFAD: 1.16 -> 0.97Helped
increase neighborhood size from 2 to 10 with simple, unselected message passing (ablation)SR-LSTM simple refinement · ETH/UCY AVGMAD: 0.50 -> 0.52Hurt
increase neighborhood size from 2 to 10 with both selection modules (ablation)SR-LSTM · ETH/UCY AVGMAD: 0.49 -> 0.46Helped
use current rather than previous neighboring hidden states (ablation)SR-LSTM · ETH/UCY AVGMAD: 0.49 -> 0.46Helped
add a second refinement iteration (ablation)SR-LSTM · ETH/UCY AVGFAD: 0.97 -> 0.94Helped
add a third refinement iteration after two iterations (ablation)SR-LSTM · ETH/UCY AVGMAD: 0.45 -> 0.45No clear effect
replace relative position with normalized absolute position (ablation)V-LSTM · ETH/UCY AVGMAD: 0.76 -> 0.63Helped
correct ETH-univ frame rate (ablation)V-LSTM · ETH/UCY AVGMAD: 0.63 -> 0.55Helped
add random rotation to frame-rate-corrected preprocessing (ablation)V-LSTM · ETH/UCY AVGMAD: 0.55 -> 0.53Helped

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