G

Git Large Textvqa

Developed by microsoft
GIT is a vision-language model based on a Transformer decoder, trained with dual conditioning on CLIP image tokens and text tokens, specifically optimized for TextVQA tasks.
Downloads 62
Release Time : 1/2/2023

Model Overview

The GIT model processes image tokens through bidirectional attention mechanisms and combines them with causal attention masks for text generation, suitable for tasks such as image captioning, visual question answering, and image classification.

Model Features

Multimodal Processing Capability
Simultaneously processes image and text inputs to achieve cross-modal understanding and generation.
Bidirectional Image Attention
Employs bidirectional attention mechanisms for image tokens to fully capture visual features.
Causal Text Generation
Uses causal attention masks during text generation to ensure the rationality of autoregressive predictions.

Model Capabilities

Image Caption Generation
Visual Question Answering
Image Classification (via text generation)

Use Cases

Visual Understanding
Image Content Question Answering
Answers complex questions about text content within images
Performs excellently in TextVQA tasks (specific metrics available in the paper)
Assistive Technology
Visual Impairment Assistance
Generates textual descriptions of image content for visually impaired users
Featured Recommended AI Models
AIbase
Empowering the Future, Your AI Solution Knowledge Base
Š 2025AIbase