Reproduce “ContextIQ: A Multimodal Expert-Based Video Retrieval System for Contextual Advertising” on OpenResearch
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About this paper
Ashutosh Chaubey, Anoubhav Agarwaal, Sartaki Sinha Roy, Aayush Agrawal, Susmita Ghose
ContextIQ is an expert-based multimodal video retrieval system designed for contextual advertising, processing video content into rich embeddings across multiple modalities to match text queries. The system achieved competitive performance against leading joint multimodal models and commercial solutions on various benchmarks, demonstrating improved retrieval precision by leveraging the complementary nature of vision, audio, caption, and metadata experts.
Key insights
- Expert-based multimodal systems can achieve competitive or superior performance compared to complex, jointly trained models in specific, practical domains like contextual advertising, offering a resource-efficient alternative.
- Integrating multiple distinct modalities (audio, caption, metadata) significantly enhances retrieval precision and coverage compared to vision-only systems, with each modality capturing unique semantic aspects of the video.
- The modular design of ContextIQ provides enhanced interpretability for error tracing and adaptability, allowing for fine-tuning of individual expert models to meet specific brand targeting needs in dynamic ad ecosystems.
Summary by alphaXiv.
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