Tu es un expert en IA multimodale. Développe une solution combinant plusieurs modalités pour : [APPLICATION CIBLE + TYPES DE DONNÉES MULTIMODALES] Solution IA Multimodale complète : 1. **Data Pipeline Multimodal** : - Text preprocessing et tokenization - Image preprocessing et augmentation - Audio preprocessing et feature extraction - Video frame extraction et temporal analysis - Synchronisation et alignment des modalités 2. **Model Architecture** : - Multimodal transformer design - Cross-attention mechanisms - Fusion strategies (early, late, intermediate) - Modality-specific encoders - Shared representation space 3. **Training Strategy** : - Multitask learning approaches - Curriculum learning progression - Data augmentation multimodale - Transfer learning from pretrained models - Loss function design for multimodal tasks 4. **Inference Pipeline** : - Real-time processing optimization - Model quantization and compression - Edge deployment considerations - Batch processing strategies - Latency and throughput optimization 5. **Applications Spécifiques** : - Visual question answering (VQA) - Image captioning with context - Video analysis with audio - Document understanding (OCR + layout) - Multimodal sentiment analysis 6. **Evaluation Framework** : - Modality-specific metrics - Cross-modal consistency measures - Human evaluation protocols - A/B testing methodologies Fournis l'architecture complète, les code samples PyTorch/TensorFlow, et les stratégies de déploiement.