Vision and Language Pre-Trained Models
VL-T5
Introduced by Cho et al. in Unifying Vision-and-Language Tasks via Text GenerationVL-T5 is a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation. The model learns to generate labels in text based on the visual and textual inputs. In contrast to other existing methods, the framework unifies tasks as generating text labels conditioned on multimodal inputs. This allows the model to tackle vision-and-language tasks with unified text generation objective. The models use text prefixes to adapt to different tasks.
Source: Unifying Vision-and-Language Tasks via Text Generation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Captioning | 3 | 16.67% |
| Text Generation | 2 | 11.11% |
| Visual Question Answering (VQA) | 2 | 11.11% |
| Human-Object Interaction Detection | 1 | 5.56% |
| Image Retrieval | 1 | 5.56% |
| Object Categorization | 1 | 5.56% |
| Object Localization | 1 | 5.56% |
| Conditional Text Generation | 1 | 5.56% |
| Language Modelling | 1 | 5.56% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
