Multi-Modal Methods
Parts, Poses, and Occlusions in 3D Visual Question Answering
Introduced by Wang et al. in 3D-Aware Visual Question Answering about Parts, Poses and OcclusionsA VQA model that marries two powerful ideas: probabilistic neural symbolic program execution for reasoning and a deep neural network with 3D generative representations of objects for robust visual scene parsing.
Source: 3D-Aware Visual Question Answering about Parts, Poses and Occlusions
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Question Answering | 1 | 33.33% |
| Visual Question Answering | 1 | 33.33% |
| Visual Question Answering (VQA) | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
