Image Representations
VirTex
Introduced by Desai et al. in VirTex: Learning Visual Representations from Textual AnnotationsVirText, or Visual representations from Textual annotations is a pretraining approach using semantically dense captions to learn visual representations. First a ConvNet and Transformer are jointly trained from scratch to generate natural language captions for images. Then, the learned features are transferred to downstream visual recognition tasks.
Source: VirTex: Learning Visual Representations from Textual Annotations
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Reconstruction | 1 | 12.50% |
| Quantization | 1 | 12.50% |
| General Classification | 1 | 12.50% |
| Image Captioning | 1 | 12.50% |
| Image Classification | 1 | 12.50% |
| Instance Segmentation | 1 | 12.50% |
| Object Detection | 1 | 12.50% |
| Semantic Segmentation | 1 | 12.50% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
