Multimodal NLP

Chapter 44: Vision-Language Models Image-text alignment, visual grounding Models: CLIP, ViLBERT, MLLMs (Multimodal LLMs) [Contrastive learning, image captioning, visual question answering] References Chapter 45: Speech-Text Integration Speech recognition, text-to-speech, cross-modal alignment Models: Whisper, wav2vec, Tacotron [ASR (Automatic Speech Recognition), TTS (Text-to-Speech), end-to-end systems] References Chapter 46: Multimodal Applications Multimodal dialogue, video understanding, embodied AI Applications: Virtual assistants, autonomous navigation, content moderation [Video captioning, multimodal sentiment analysis, gesture recognition] References Chapter 47: Multimodal Pretraining and Evaluation Multimodal datasets, pretraining objectives Evaluation: Cross-modal retrieval, zero-shot performance [Flamingo, MURAL, VQA benchmarks, robustness testing] References