Object Detection Models
MDETR
Introduced by Kamath et al. in MDETR -- Modulated Detection for End-to-End Multi-Modal UnderstandingMDETR is an end-to-end modulated detector that detects objects in an image conditioned on a raw text query, like a caption or a question. It utilizes a transformer-based architecture to reason jointly over text and image by fusing the two modalities at an early stage of the model. The network is pre-trained on 1.3M text-image pairs, mined from pre-existing multi-modal datasets having explicit alignment between phrases in text and objects in the image. The network is then fine-tuned on several downstream tasks such as phrase grounding, referring expression comprehension and segmentation.
Source: MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Referring Expression | 4 | 9.76% |
| Visual Question Answering (VQA) | 4 | 9.76% |
| Phrase Grounding | 3 | 7.32% |
| Question Answering | 3 | 7.32% |
| Visual Question Answering | 3 | 7.32% |
| Visual Grounding | 3 | 7.32% |
| Object Detection | 2 | 4.88% |
| Referring Expression Segmentation | 2 | 4.88% |
| Object | 2 | 4.88% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
