ViLT: Vision-and-Language Transformer
| Property | Value |
|---|---|
| Author | dandelin |
| Paper | ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision |
| Training Data | GCC+SBU+COCO+VG (200k steps) |
What is vilt-b32-mlm-itm?
ViLT (Vision-and-Language Transformer) is an innovative model that processes both visual and textual information without requiring traditional convolutional neural networks or region-based supervision. This implementation, developed by Kim et al., represents a significant advancement in multimodal AI processing.
Implementation Details
The model employs a transformer-based architecture that directly processes image patches and text tokens in a unified manner. It was pre-trained for 200,000 steps on a diverse dataset combination including GCC, SBU, COCO, and Visual Genome.
- Efficient architecture without conventional CNN components
- Direct patch-based image processing
- Integrated MLM (Masked Language Modeling) and ITM (Image-Text Matching) objectives
Core Capabilities
- Visual Question Answering
- Image-Text Matching
- Masked Language Modeling
- Multimodal Understanding
Frequently Asked Questions
Q: What makes this model unique?
ViLT's distinctiveness lies in its ability to process vision and language tasks without conventional convolutional networks or region supervision, making it more efficient while maintaining performance.
Q: What are the recommended use cases?
The model is primarily designed for visual question answering tasks and can be effectively used for any application requiring joint understanding of images and text.
