Vision and Language Pre-Trained Models
Simple Visual Language Model
Introduced by Wang et al. in SimVLM: Simple Visual Language Model Pretraining with Weak SupervisionSimVLM is a minimalist pretraining framework to reduce training complexity by exploiting large-scale weak supervision. It is trained end-to-end with a single prefix language modeling (PrefixLM) objective. PrefixLM enables bidirectional attention within the prefix sequence, and thus it is applicable for both decoder-only and encoder-decoder sequence-to-sequence language models.
Source: SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Captioning | 2 | 11.11% |
| Visual Question Answering | 2 | 11.11% |
| Visual Question Answering (VQA) | 2 | 11.11% |
| Language Modelling | 2 | 11.11% |
| Action Classification | 1 | 5.56% |
| Image Classification | 1 | 5.56% |
| Retrieval | 1 | 5.56% |
| Video Retrieval | 1 | 5.56% |
| Visual Entailment | 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 |
