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Zero-shot image classification assigns natural-language labels to visual content without custom training. You provide an image and candidate text categories, then the system scores matches using cosine similarity between normalized image and text embeddings.

Semantic image search matches text queries like “sunset over ocean” to images. It requires text tokenization and image preprocessing to generate embeddings, with optional vector database integration for cross-modal ranking across large image collections.

Cross-modal retrieval matches text queries to images or finds captions describing an image. It computes cosine similarity scores between normalized vision-language embeddings, enabling bidirectional search across visual and textual content.

Duplicate detection workflows benefit from optional vector database integration to index and query large image collections efficiently. For smaller datasets, direct embedding normalization and cosine similarity scoring can identify near-duplicate images without a vector database.

Content moderation and visual question answering are supported workflows. By scoring image-text similarity, the system screens visual content against moderation labels and matches images to descriptive text queries for answering visual questions.

Zero-shot classification depends on the quality of provided text labels and image preprocessing. Highly specialized or domain-specific visual categories may yield lower accuracy without carefully chosen natural-language candidate labels.