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Zero-shot image classification categorizes images using natural language prompts without fine-tuning by mapping both modalities into a joint embedding space. It computes cosine similarity between image and text features to identify the best matching descriptions.

Cross-modal retrieval enables searching images by text or finding descriptive captions for images by measuring semantic similarity. It works by extracting features from both images and text, mapping them into a shared embedding space, and computing cosine similarity to find matches across modalities.

Yes, running CLIP inference requires transformers, torch, and pillow installed in your environment. These libraries provide model loading, feature extraction, and cosine similarity computation capabilities needed for zero-shot classification and image-text matching tasks.

Yes, CLIP supports content moderation and visual question answering across diverse domains without fine-tuning. It measures semantic similarity between images and natural language descriptions within a joint embedding space to enable flexible vision-language tasks.

Image-text similarity is measured by extracting features from both modalities and computing cosine similarity between their embeddings. CLIP maps images and captions into a shared joint embedding space, enabling direct semantic comparison without fine-tuned classification heads.

Zero-shot classification may not suit tasks requiring high precision on specialized domains or fine-grained categories not well represented in pretraining data. Without fine-tuning, CLIP relies on semantic similarity between prompts and images, limiting accuracy for highly specific or custom classification needs.