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Video Llava

Developed by AnasMohamed
A large-scale vision-language model based on Vision Transformer architecture, supporting cross-modal understanding between images and text
Text-to-Image#Multimodal Understanding#Zero-shot Classification#Image-Text Matching
Downloads 194
Release Time : 6/14/2024

Model Overview

This model is a variant of the CLIP series, using ViT-Large architecture with 336x336 pixel input size, capable of understanding image content and associating it with text descriptions

Model Features

Large-scale Pretraining
Pretrained on a vast number of image-text pairs to learn rich visual concept representations
Cross-modal Understanding
Capable of processing and understanding both visual and textual information, achieving semantic alignment between images and text
Zero-shot Capability
Can perform various visual understanding tasks without task-specific fine-tuning

Model Capabilities

Image Classification
Image-Text Matching
Cross-modal Retrieval
Visual Question Answering
Image Caption Generation

Use Cases

Content Retrieval
Text-based Image Search
Find relevant images using natural language descriptions
Content Moderation
Inappropriate Content Detection
Identify image content that does not match specific text descriptions
Creative Assistance
Image Annotation
Automatically generate text descriptions for images
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