Efficient Logic Gate Networks for Video Copy Detection

📰 ArXiv cs.AI

Learn to build efficient video copy detection systems using Logic Gate Networks, reducing computational costs and descriptor sizes

advanced Published 25 Apr 2026

Action Steps

Build a differentiable Logic Gate Network (LGN) using a deep learning framework
Replace conventional floating-point feature extractors with LGNs
Train the LGN-based video copy detection model on a large-scale dataset
Evaluate the performance of the LGN-based model using metrics such as accuracy and computational cost
Optimize the LGN architecture for improved efficiency and scalability

Who Needs to Know This

Computer vision engineers and researchers can benefit from this approach to improve video copy detection systems, while product managers can consider the potential for more efficient and scalable solutions

Key Insight

💡 Logic Gate Networks can be used to build efficient video copy detection systems, reducing computational costs and descriptor sizes while maintaining strong performance

Key Takeaways

Learn to build efficient video copy detection systems using Logic Gate Networks, reducing computational costs and descriptor sizes

Full Article

Title: Efficient Logic Gate Networks for Video Copy Detection

Abstract:
arXiv:2604.21694v1 Announce Type: cross Abstract: Video copy detection requires robust similarity estimation under diverse visual distortions while operating at very large scale. Although deep neural networks achieve strong performance, their computational cost and descriptor size limit practical deployment in high-throughput systems. In this work, we propose a video copy detection framework based on differentiable Logic Gate Networks (LGNs), which replace conventional floating-point feature ext

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