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Cascaded Prediction Network via Segment Tree for Temporal Video Grounding

CVPR 2021 · Video › Video-language understanding · approach: Temporal video grounding · 85 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use a cross-gate module instead of 1D convolution or 1D max-pooling for tree initialization (ablation)CPN · ActivityNet Caption, Charades-STA, and TACoSgrounding performanceHelped
Add signal-loss supervision from inside target segments (ablation)CPN · ActivityNet Caption, Charades-STA, and TACoSgrounding performanceHelped
Add message passing to the initialized segment tree (ablation)CPN · ActivityNet Caption, Charades-STA, and TACoSgrounding performanceHelped
Set signal-loss balance factor λ to 6 (ablation)CPN · ActivityNet Caption and Charades-STAgrounding performanceHelped
Set signal-loss balance factor λ to 8 (ablation)CPN · TACoSgrounding performanceHelped
Increase the signal-loss weight from too small a value toward the dataset-specific optimum (ablation)CPN · ActivityNet Caption, Charades-STA, and TACoSgrounding performanceHelped
Increase the signal-loss weight beyond the dataset-specific optimum (ablation)CPN · ActivityNet Caption, Charades-STA, and TACoSgrounding performanceHurt
Use fine-tuned rather than non-fine-tuned visual featuresCPN · Charades-STAgrounding performanceHelped
Use 3D ConvNet rather than 2D ConvNet visual featuresCPN · Charades-STAgrounding performanceHelped

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