Transform how AI systems understand and connect different data modalities. This course empowers machine learning professionals to build cutting-edge cross-modal retrieval systems that bridge the gap between text and images. You'll master the technical implementation of approximate nearest-neighbor search algorithms and design sophisticated attention mechanisms that fuse visual and textual information. Through hands-on work with production-scale tools like FAISS and real datasets like Flickr30K, you'll develop the expertise to create intelligent systems that understand content across modalities—enabling breakthrough applications in search, recommendation, and content understanding that mirror how humans naturally process diverse information types.
Unify Modalities: Cross-Modal Retrieval
Unify Modalities: Cross-Modal Retrieval
本课程是 Vision & Audio AI Systems 专项课程 的一部分
位教师：Hurix Digital
访问权限由 New York State Department of Labor 提供
您将学到什么
Cross-modal retrieval aligns vector spaces to bridge semantic gaps between text, images, and other data types.
ANN tools like FAISS enable fast similarity search across millions of embeddings with production-scale performance.
Attention mechanisms fuse visual and textual features by learning contextual relationships across multiple representations.
Multimodal systems balance accuracy, speed, and memory through careful index choice and parameter tuning.
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该课程共有2个模块
Learners will build foundational understanding of cross-modal retrieval systems and implement approximate nearest-neighbor search algorithms using FAISS for production-scale similarity search across multimodal embeddings.
涵盖的内容
1个视频2篇阅读材料1个作业1个非评分实验室
Learners will design and implement sophisticated attention-based fusion algorithms that intelligently combine visual and textual embeddings, mastering the creation of multimodal neural architectures for advanced cross-modal AI applications.
涵盖的内容
2篇阅读材料3个作业
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