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Vilt Finetuned 200
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Vilt Finetuned 200

Developed by MariaK
This model is a vision-language model based on the ViLT architecture, fine-tuned on VQA datasets, suitable for visual question answering tasks.
Text-to-Image
Transformers
Open Source License:Apache-2.0#Visual Question Answering Fine-tuning#Multimodal Understanding#VQA Task Optimization
Downloads 84
Release Time : 8/1/2023

Model Overview

ViLT is a vision-and-language transformer model that combines visual and textual information processing capabilities. The model has been fine-tuned on VQA (Visual Question Answering) tasks, enabling it to understand image content and answer related questions.

Model Features

Multimodal Understanding
Capable of processing both visual and textual information simultaneously, achieving cross-modal understanding.
Fine-tuning Optimization
Specially fine-tuned on VQA datasets to enhance visual question answering performance.
Transformer-based Architecture
Utilizes advanced Transformer architecture for efficient cross-modal information fusion.

Model Capabilities

Visual Question Answering
Image Understanding
Cross-modal Reasoning

Use Cases

Education
Educational Assistance
Helps students understand image content in textbooks and answer related questions.
Accessibility Technology
Visual Assistance
Describes image content and answers related questions for visually impaired individuals.
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