Multimodal AI with Vision-Language Models like CLIP

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Multimodal AI with Vision-Language Models like CLIP

Contrastive Language-Image Pretraining (CLIP) learns joint embeddings by contrasting positive and negative image-text pairs using a dual-encoder architecture.

 Key Uses:

Zero-shot image classification, content moderation, and visual search.

 Hacks:

 Use CoOp (Context Optimization) to learn continuous prompt vectors. Fine-tune with LoRA on custom datasets for domain adaptation. Combine with visual question answering (VQA) pipelines. Leverage CLIP embeddings for unsupervised clustering of visual data.

 References:

 Radford, A., et al. (2021). Learning Transferable Visual Models From Natural Language Supervision. ICML.

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