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Introduced by Le et al. in Hierarchical Conditional Relation Networks for Video Question Answering
Conditional Relation Network, or CRN, is a building block to construct more sophisticated structures for representation and reasoning over video. CRN takes as input an array of tensorial objects and a conditioning feature, and computes an array of encoded output objects. Model building becomes a simple exercise of replication, rearrangement and stacking of these reusable units for diverse modalities and contextual information. This design thus supports high-order relational and multi-step reasoning.
Source: Hierarchical Conditional Relation Networks for Video Question Answering
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Speech Enhancement | 7 | 17.50% |
| Decoder | 4 | 10.00% |
| Epidemiology | 2 | 5.00% |
| Decision Making | 2 | 5.00% |
| Image Enhancement | 2 | 5.00% |
| Low-Light Image Enhancement | 2 | 5.00% |
| Question Answering | 2 | 5.00% |
| Relation Network | 2 | 5.00% |
| Video Question Answering | 2 | 5.00% |
Usage Over Time
This feature is experimental; we are continuously improving our matching algorithm.
Components
| Component | Type | Add Remove |
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
Categories Add Remove
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