Vision and Language Pre-Trained Models
One Representation
Introduced by Jang et al. in Unifying Vision-Language Representation Space with Single-tower TransformerIn the OneR method, model input can be one of image, text or image+text, and CMC objective is combined with the traditional image-text contrastive (ITC) loss. Masked modeling is also carried out for all three input types (i.e., image, text and multi-modal). This framework employs no modality-specific architectural component except for the initial token embedding layer, making our model generic and modality-agnostic with minimal inductive bias.
Source: Unifying Vision-Language Representation Space with Single-tower Transformer
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Anomaly Detection | 1 | 25.00% |
| Object Localization | 1 | 25.00% |
| Retrieval | 1 | 25.00% |
| Visual Reasoning | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
