Research map / Learning theory and algorithms / Prediction and decision losses
Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use recurrent decision trees that combine a learned control signal with an autoregressor, rather than the tested conventional baselines | Realtime camera planning · Basketball | joint loss | Helped |
| Use recurrent decision trees that combine a learned control signal with an autoregressor, rather than the tested conventional baselines | Realtime camera planning · Soccer | joint loss | Helped |
| Use the proposed method instead of the tested baselines | Realtime camera planning · Basketball | user preference | Helped |
| Use the proposed method instead of the tested baselines | Realtime camera planning · Soccer | user preference | No clear effect |
| Add autoregressor influence by increasing λ (ablation) | Recurrent decision tree camera planner · Basketball | smoothness | Helped |
| Add stronger autoregressor influence by making λ very large (ablation) | Recurrent decision tree camera planner · Basketball | accuracy | Hurt |
| Remove autoregressor influence by setting λ to zero (ablation) | Recurrent decision tree camera planner · Basketball | smoothness | Hurt |
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