Research map / Large language models / AI and society
Crowd-Assisted Disaster Scene Assessment with Human-AI Interactive Attention
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add crowd task generation (uncertainty and diversity query selection) (ablation) | iDSA · Ecuador Earthquake test (trained on Nepal) | F1: 0.838 -> 0.867 | Helped |
| add interactive attention CNN (IACNN) (ablation) | iDSA · Ecuador Earthquake test (trained on Nepal) | F1: 0.824 -> 0.867 | Helped |
| add social media image normalization (SMIN) (ablation) | iDSA · Ecuador Earthquake test (trained on Nepal) | F1: 0.816 -> 0.867 | Helped |
| add budget constrained adaptive incentive (bandit) (ablation) | iDSA · Ecuador Earthquake test (trained on Nepal) | F1: 0.842 -> 0.867 | Helped |
| use adaptive bandit incentive instead of fixed incentive policy (ablation) | iDSA · Ecuador Earthquake test (trained on Nepal) | crowd delay (s): 527.61 -> 427.81 (vs Hybrid-AL) | Helped |
| increase share of images sent to crowd from 5% to 25% (ablation) | iDSA · Ecuador Earthquake test (trained on Nepal) | attention IOU: 0.549 -> 0.598 | Helped |
| use human-AI interactive attention instead of retraining on crowd labels (Hybrid-AL) (ablation) | iDSA · Ecuador Earthquake test (trained on Nepal) | F1: 0.818 -> 0.867 | Helped |
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