Vision and Language Pre-Trained Models
Visual Parsing
Introduced by Xue et al. in Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-trainingVisual Parsing is a vision and language pretrained model that adopts self-attention for visual feature learning where each visual token is an approximate weighted mixture of all tokens. Thus, visual parsing provides the dependencies of each visual token pair. It helps better learning of visual relation with the language and promote inter modal alignment. The model is composed of a vision Transformer that takes an image as input and outputs the visual tokens and a multimodal Transformer. It applies a linear layer and a Layer Normalization to embed the vision tokens. It follows BERT to get word embeddings. Vision and language tokens are concatenated to form the input sequences. A multi-modal Transformer is used to fuse the vision and language modality. A metric named Inter-Modality Flow (IMF) is used to quantify the interactions between two modalities. Three pretraining tasks are adopted: Masked Language Modeling (MLM), Image-Text Matching (ITM), and Masked Feature Regression (MFR). MFR is a novel task that is included to mask visual tokens with similar or correlated semantics in this framework.
Source: Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Language Modelling | 2 | 11.11% |
| Question Answering | 2 | 11.11% |
| Visual Question Answering | 2 | 11.11% |
| Visual Question Answering (VQA) | 2 | 11.11% |
| Image Retrieval | 1 | 5.56% |
| Interactive Segmentation | 1 | 5.56% |
| Described Object Detection | 1 | 5.56% |
| Large Language Model | 1 | 5.56% |
| Pose Estimation | 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 |
