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SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation

CVPR 2021 · Video › Video segmentation and tracking · approach: Mask propagation for unsupervised video segmentation · 188 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add sinusoidal positional encodings for X, Y, and T (ablation)SST · DAVIS 2017 validationJ & F (%): $73\pm 2$ -> $75.6\pm 0.6$Helped
Increase temporal history from τ=1 to τ=2 (ablation)SST · DAVIS 2017 validationJ & F (%): $75.8\pm 0.3$ -> $76.2\pm 0.3$Helped
Increase temporal history from τ=2 to τ=3 (ablation)SST · DAVIS 2017 validationJ & F (%): $76.2\pm 0.3$ -> $76.5\pm 0.3$Helped
Increase grid-attention depth from 1 to 2 layers (ablation)SST · DAVIS 2017 validationJ & F (%): 65.3 -> 66.1Helped
Increase grid-attention depth from 2 to 3 layers (ablation)SST · DAVIS 2017 validationJ & F (%): 66.1 -> 64.2Hurt
Use local attention instead of grid attention (ablation)SST · DAVIS 2017 validationJ & F (%): 66.1 -> 76.2Helped
Use strided attention instead of grid attention (ablation)SST · DAVIS 2017 validationJ & F (%): 66.1 -> 69.5Helped
Use LocalStrided attention instead of grid attention (ablation)SST · DAVIS 2017 validationJ & F (%): 66.1 -> 72.3Helped
Use sinusoidal positional encoding instead of no positional encoding (ablation)SST · DAVIS 2017 validationJ & F (%): $73\pm 2$ -> $75.6\pm 0.6$Helped

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