Multi-Modal Methods
Multiscale Attention ViT with Late fusion
Introduced by Maaz et al. in Class-agnostic Object Detection with Multi-modal TransformerMultiscale Attention ViT with Late fusion (MAVL) is a multi-modal network, trained with aligned image-text pairs, capable of performing targeted detection using human understandable natural language text queries. It utilizes multi-scale image features and uses deformable convolutions with late multi-modal fusion. The authors demonstrate excellent ability of MAVL as class-agnostic object detector when queried using general human understandable natural language command, such as "all objects", "all entities", etc.
Source: Class-agnostic Object Detection with Multi-modal TransformerPapers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Open Vocabulary Attribute Detection | 1 | 14.29% |
| Open Vocabulary Object Detection | 1 | 14.29% |
| Zero-Shot Object Detection | 1 | 14.29% |
| Class-agnostic Object Detection | 1 | 14.29% |
| Object Detection | 1 | 14.29% |
| Object Proposal Generation | 1 | 14.29% |
| Open World Object Detection | 1 | 14.29% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
