Text-to-Video Semantic Search

Built text-to-video semantic scene retrieval with multilingual query processing. This project demonstrates practical execution from architecture and implementation to measurable delivery outcomes.

Personal ProjectsYear 2026

Project Overview

Objective

Built text-to-video semantic scene retrieval with multilingual query processing.

Stack

OpenCVCLIP (ViT-B/32)FAISSFastAPIReact.jsTailwind CSS

Delivery highlights

  • Built an end-to-end text-to-image semantic search system that enables users to search for images using natural language instead of keyword matching by leveraging CLIP (ViT-B/32) to encode both text and images into a shared embedding space and indexing precomputed image embeddings with FAISS for efficient similarity search. Developed a FastAPI backend to process queries and return matched image URLs with similarity scores, along with a React.js frontend for real-time search and dynamic result visualization.
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