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Image Captioning Model

Developed by premanthcharan
A model combining Vision Transformer (ViT) with natural language processing to automatically generate natural language descriptions for input images
Downloads 28
Release Time : 11/12/2024

Model Overview

This model achieves image-to-text conversion through a vision encoder-decoder architecture, utilizing ResNet101 for feature extraction and a multi-layer transformer structure. Trained on the MS COCO dataset, it supports high-quality image caption generation

Model Features

Vision-Language Joint Modeling
Achieves deep association between image features and text descriptions through end-to-end training
Attention Mechanism Optimization
Uses multi-head attention with positional encoding to accurately capture key image regions and their textual correspondences
Multi-metric Evaluation System
Supports automatic quality assessment across multiple dimensions including BLEU, METEOR, and CIDEr

Model Capabilities

Image Understanding
Natural Language Generation
Scene Description
Multimodal Processing

Use Cases

Assistive Technology
Visual Impairment Assistance
Automatically describes surroundings for visually impaired users
Enhances environmental awareness for the visually impaired
Content Management
Automatic Image Tagging
Generates search tags for large image collections
Improves image retrieval efficiency
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