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14. Visual Question Answering

Authors
Affiliations
Birmingham City University
Sunway College Kathmandu

Objective

Answers questions about image content by combining vision and language models, requiring both image understanding and reasoning.

System Architecture

[Mermaid diagram - flowchart showing core components and data flow]

[3-5 sentence description of architecture]

Technical Approach

Key Components

Pipeline / Data Flow

[Detailed description of request → processing → response flow]

Complexity Analysis

MetricComplexityNotes
Model sizeVision: 100M-1B, Language: 1B-7B[implications]
Time complexityO(visual_tokens × seq_len)[notes]
Space complexity~2-10GB[notes]
Latency targetp95 <500ms per question[real-time vs. batch]
Throughput target50-200 q/s[per GPU/instance]

Pros & Cons

Pros

Cons

Trade-offs

[1-2 paragraphs discussing key technical trade-offs]

Real-World Applications

Where This Pattern Appears

Production Considerations

[2-3 paragraphs on scaling, failure modes, monitoring, cost]

References & Citations

Citation 1: Architecture & Design

Title: [Paper/Blog Title on Visual Question Answering Architecture]

Citation 2: Performance & Benchmarks

Title: [Performance Benchmarks for Visual Question Answering]

Citation 3: Implementation Details

Title: [Implementation Details and Trade-offs]

Citation 4: Real-World Deployment

Title: [Production Deployment Insights]

Reproducibility Checklist