Research map / Vision-language and multimodal / Cross-modal learning
Spatiotemporal Activity Modeling Under Data Scarcity: A Graph-Regularized Cross-Modal Embedding Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add branched task-specific embeddings and graph-regularized transfer (BranchNet) over Planetoid shared-embedding multitask | cross-modal embedding · LA tweets location reconstruction | MRR: 0.5812 -> 0.5904 | Helped |
| add external-source knowledge transfer via BranchNet to CrossMap | CrossMap · LA tweets time reconstruction | MRR: 0.3609 -> 0.3730 | Helped |
| add semi-supervised graph regularization (SemiEmbed) to CrossMap | CrossMap · LA tweets location reconstruction | MRR: 0.5733 -> 0.5100 | Hurt |
| smaller user corpus (10 thousand records) | BranchNet vs CrossMap · LA location prediction | MRR: +3% | Helped |
| increase main embedding dimension beyond 400 (ablation) | all methods · LA activity prediction | MRR | No clear effect |
| increase auxiliary embedding dimension beyond 100 (ablation) | BranchNet · location and text prediction | MRR | No clear effect |
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