Design large-scale near-duplicate video detection

Q: Design large-scale near-duplicate video detection

This question evaluates a Machine Learning Engineer's competency in scalable similarity search and representation learning for multimedia, including embedding design, retrieval infrastructure, compression/quantization, large-scale ANN indexing, and human-in-the-loop decision workflows.

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Question

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Design a product-grade fuzzy (near-)duplicate detection system for a large short-video platform.

You need to detect whether an uploaded video is a near-duplicate (or highly similar) to existing content at very large scale.

Address:

Feature/embedding generation (e.g., vision/audio/text signals).
Storage and retrieval for similarity search (vector database / ANN index).
How to handle very large corpora (tens/hundreds of millions+ videos) and product-level QPS .
How to support very high concurrency while keeping low latency.
Techniques such as compression / quantization and LSH/ANN .
How to reduce and handle false positives/false negatives .
If creators can appeal a takedown/duplicate decision: design a human-in-the-loop workflow to calibrate and improve the model.
How to model and quantify the trade-off between embedding dimension and end-to-end system latency (and also accuracy/recall).

Design large-scale near-duplicate video detection

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Design large-scale near-duplicate video detection

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