#multi-modal-ai

Develop multi-modal AI systems integrating vision and language for comprehensive understanding and generation tasks. Multi-modal architecture: 1. Vision encoders: ResNet, EfficientNet, Vision Transformer for image feature extraction. 2. Language encoders: BERT, RoBERTa, T5 for text understanding, tokenization strategies. 3. Fusion strategies: early fusion (concatenation), late fusion (separate processing), attention-based fusion. Vision-Language models: 1. CLIP: contrastive learning, image-text pairs, zero-shot classification, semantic search. 2. DALL-E: text-to-image generation, autoregressive transformer, discrete VAE tokenization. 3. BLIP: bidirectional encoder, unified vision-language understanding, captioning and QA. Applications: 1. Image captioning: CNN-RNN architectures, attention mechanisms, beam search decoding. 2. Visual question answering: image understanding, question reasoning, answer generation. 3. Text-to-image generation: prompt engineering, style control, quality assessment. Cross-modal retrieval: 1. Image-text matching: similarity learning, triplet loss, hard negative mining. 2. Semantic search: joint embedding space, cosine similarity, ranking optimization. 3. Few-shot learning: prototype networks, meta-learning, domain adaptation. Training strategies: 1. Contrastive learning: InfoNCE loss, negative sampling, temperature scaling. 2. Masked modeling: masked language modeling, masked image modeling, unified objectives. 3. Multi-task learning: shared representations, task-specific heads, loss balancing. Evaluation: 1. Captioning: BLEU, METEOR, CIDEr scores, human evaluation for quality. 2. VQA accuracy: exact match, fuzzy matching, answer distribution analysis. 3. Retrieval: Recall@K, Mean Reciprocal Rank, cross-modal similarity analysis.